Abstract:
A conventional method in which an amount of movement of an eye ball between acquired images is calculated by extracting characteristic images of the fundus and comparing the images is excellent in precision, reproducibility and stability, but requires time for image processing. The aforementioned problem can be solved by using a tracking apparatus including: a fundus imaging apparatus for acquiring a fundus image; and a measurement unit that extracts a characteristic image of a fundus image from a first fundus image captured by the fundus imaging apparatus, detects the characteristic image from a second fundus image that is different from the fundus image, and measures a position change in the fundus images from coordinates of the extracted characteristic image and the detected characteristic image in the respective fundus images, wherein a region in which the characteristic image is detected from the second fundus image is determined so that a region searched for the characteristic image from the first image includes the extracted characteristic image and is broader than a range of movement of the characteristic image resulting from movements of the eye ball within measurement time.

Description:
TECHNICAL FIELD  
       [0001]    The present invention relates to an ophthalmologic apparatus and a control method for the same, and specifically relates to an ophthalmologic apparatus in which an amount of movement of an eye ball is calculated and a control method for the same. 
       BACKGROUND ART  
       [0002]    In recent years, apparatuses that measure eye movements have attracted attention. If eye movements can be measured, such measurement can be applied to a visual field test, or, e.g., a tomographic imaging apparatus for a fundus, which acquires images with higher precision, enabling more accurate fundus diagnosis. 
         [0003]    For eye movement measurement methods, various techniques, such as the corneal reflection (Purkinje image) method or the search coil method, have been known. Among them, a method in which eye movements are measured from fundus images has been studied as a method that is easy and less stressful for test objects. 
         [0004]    In order to measure an eye movement with high accuracy from fundus images, it is necessary to extract a characteristic image from a fundus image, search for and detect the characteristic image in an object image and then calculate the amount of movement of the characteristic image. Among them, the step of extracting the characteristic image is important from the perspective of stability, accuracy and reproducibility of eye movement measurement. For a characteristic image in a fundus image, e.g., a macula or an optic papilla (hereinafter referred to as “papilla”) is used. Also, because e.g., an affected eye often has a defective macula or papilla, blood vessels may be used for a characteristic image in a fundus image. For a method for extracting a characteristic image of blood vessels, various methods are known. For example, Patent Literature 1 discloses a method in which the number of blood vessels and whether or not blood vessels exist in a center portion of a filter set in a fundus image are determined from average values of pixel values in an outer peripheral portion of the filter to determine whether or not a blood vessel crossing part exists in the filter region. 
       CITATION LIST  
     Patent Literature 
       [0005]    PTL 1: Japanese Patent Application Laid-Open No. 2001-70247 
       SUMMARY OF INVENTION  
     Technical Problem 
       [0006]    Using a method as in Patent Literature 1, a characteristic image of a fundus is extracted, and the positions of the characteristic image are compared between images to calculate the amount of movement of the eye ball between the acquired images, which enables the amount of movement of the eye ball to be detected with high accuracy from the fundus images. 
         [0007]    However, in such method, the entire areas of the acquired fundus images are searched to extract characteristic images to be compared. 
         [0008]    Accordingly, a problem arises in that the image processing is unnecessarily time consuming. Furthermore, another problem arises in that where an extracted characteristic image is located at an edge portion of an acquired image, the characteristic image may fall outside a processing-object image due to a movement of the eye ball, resulting in impossibility of detecting the amount of movement of the eye-ball (i.e. characteristic image search error). Furthermore, when detection of a rotational movement of an eye ball is intended, if a characteristic image is extracted from a center portion of an image, no change is caused in position of the characteristic image resulting from the rotation of the eye ball, which may result in impossibility of detecting the rotation. 
       Solution to Problem 
       [0009]    In order to solve the aforementioned problems, an ophthalmologic apparatus for detecting an amount of a movement of an eye to be inspected, according to a first configuration of the present invention includes: an image acquiring unit that acquires a plurality of fundus images of the eye to be inspected, at different times; a processing unit that sets a partial region from at least one fundus image from among the plurality of acquired fundus images, based on an eye movement amount performs processing, the processing being at least one of extraction and search of at least one characteristic image for the set partial region; and a detecting unit that detects a position change in the plurality of fundus images based on a result of the processing performed by the processing unit. 
         [0010]    A method for detecting an amount of movement of an eye to be inspected, according to a second configuration of the present invention, includes the steps of: acquiring a plurality of fundus images of the eye to be inspected, at different times; performing processing including extraction of a characteristic image from each of at least two fundus images from among the plurality of acquired fundus images and calculation of a coordinate difference between the extracted characteristic images; detecting a position change in the plurality of fundus images based on a result of the processing; and setting a partial region for at least one fundus image from among the plurality of acquired fundus images, based on an eye movement amount. 
       Advantageous Effects of Invention 
       [0011]    According to the present invention, a region matching a characteristic image can efficiently and reliably be found within a processing-object image, enabling an increase in speed of template matching. 
         [0012]    Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0013]      FIG. 1  is a schematic diagram illustrating a configuration of an optical system of a fundus camera in example 1 of the present invention. 
           [0014]      FIG. 2  is a schematic diagram of a functional architecture of an apparatus in example 1 of the present invention. 
           [0015]      FIG. 3  is a flowchart of a control flow in example 1 of the present invention. 
           [0016]      FIG. 4  is a flow chart relating to processing A in the control flow in example 1 of the present invention. 
           [0017]      FIG. 5  is a flow diagram relating to processing B in the control flow in example 1 of the present invention. 
           [0018]      FIG. 6A  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0019]      FIG. 6B  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0020]      FIG. 6C  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0021]      FIG. 6D  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0022]      FIG. 6E  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0023]      FIG. 6F  is a schematic diagram illustrating a fundus image in example 1 of the present invention. 
           [0024]      FIG. 7A  is a schematic diagram relating to a matching region in example 1 of the present invention. 
           [0025]      FIG. 7B  is a schematic diagram relating to a matching region in example 1 of the present invention. 
           [0026]      FIG. 7C  is a schematic diagram relating to a matching region in example 1 of the present invention. 
           [0027]      FIG. 8  is a schematic diagram illustrating eye movements in the present invention. 
           [0028]      FIG. 9  is a schematic diagram of a graph relating to eye movements and time in the present invention. 
           [0029]      FIG. 10A  is a schematic diagram relating to template matching in example 1 of the present invention. 
           [0030]      FIG. 10B  is a schematic diagram relating to template matching in example 1 of the present invention. 
           [0031]      FIG. 11  is a schematic diagram illustrating configurations of optical systems in an OCT apparatus and an SLO apparatus in example 2 of the present invention. 
           [0032]      FIG. 12  is a schematic diagram of a functional architecture in an apparatus in example 2 of the present invention. 
           [0033]      FIG. 13  is a flowchart of a control flow in example 2 of the present invention. 
           [0034]      FIG. 14  is a flowchart relating to processing C in the control flow in example 2 of the present invention. 
           [0035]      FIG. 15  is a flowchart relating to processing D in the control flow in example 2 of the present invention. 
           [0036]      FIG. 16A  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0037]      FIG. 16B  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0038]      FIG. 16C  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0039]      FIG. 16D  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0040]      FIG. 16E  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0041]      FIG. 16F  is a schematic diagram illustrating an SLO fundus image in example 2 of the present invention. 
           [0042]      FIG. 17A  is a schematic diagram relating to a matching region in example 2 of the present invention. 
           [0043]      FIG. 17B  is a schematic diagram relating to a matching region in example 2 of the present invention. 
           [0044]      FIG. 17C  is a schematic diagram relating to a matching region in example 2 of the present invention. 
           [0045]      FIG. 18A  is a schematic diagram relating to template matching in example 2 of the present invention. 
           [0046]      FIG. 18B  is a schematic diagram relating to template matching in example 2 of the present invention. 
           [0047]      FIG. 19  is a schematic diagram indicating an example of display in example 2 of the present invention. 
           [0048]      FIG. 20  is a flowchart of a control flow in example 3 of the present invention. 
           [0049]      FIG. 21  is a flowchart relating to processing E in the control flow in example 3 of the present invention. 
           [0050]      FIG. 22  is a flowchart relating to processing F in the control flow in example 3 of the present invention. 
           [0051]      FIG. 23A  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0052]      FIG. 23B  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0053]      FIG. 23C  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0054]      FIG. 23D  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0055]      FIG. 23E  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0056]      FIG. 23F  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0057]      FIG. 23G  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0058]      FIG. 23H  is a schematic diagram illustrating an SLO fundus image in example 3 of the present invention. 
           [0059]      FIG. 24  is a flowchart of a control flow in example 4 of the present invention. 
           [0060]      FIG. 25  is a flowchart relating to processing G in the control flow in example 4 of the present invention. 
           [0061]      FIG. 26A  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0062]      FIG. 26B  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0063]      FIG. 26C  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0064]      FIG. 26D  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0065]      FIG. 26E  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0066]      FIG. 26F  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0067]      FIG. 26G  is a schematic diagram illustrating a fundus image in example 4 of the present invention. 
           [0068]      FIG. 27  is a flowchart of a control flow in example 5 of the present invention. 
           [0069]      FIG. 28A  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0070]      FIG. 28B  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0071]      FIG. 28C  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0072]      FIG. 28D  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0073]      FIG. 28E  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0074]      FIG. 28F  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0075]      FIG. 28G  is a schematic diagram illustrating an SLO image in example 5 of the present invention. 
           [0076]      FIG. 29  is a schematic diagram indicating a fundus image in example 6 of the present invention. 
       
    
    
     DESCRIPTION OF EMBODIMENTS  
       [0077]    Modes for setting a partial region according to the present invention will be described in details in the following examples with reference to the drawings. Examples 1 to 3 will be described in terms of an example where in a fundus image acquiring apparatus, a characteristic image in a fundus image is extracted, and then a region to be searched for the extracted characteristic image within another object image is adjusted, thereby enhancing the processing speed. Examples 4 to 6 will be described in terms of an example where when extracting a characteristic image in a fundus image, an extracting region is designated, enabling more efficient and reliable eye movement measurement. 
         [0078]    Although the below examples will be described in terms of an example where the present invention is applied to a single apparatus, the subject matter of the present invention is not limited to any of the configurations described below, and is either not limited to a single apparatus including any of the configurations described below. The present invention can be provided by use of a method for providing functions described below, and processing for supplying software (computer program) providing such functions to a system or an apparatus via a network or various types of recording media and causing a computer (or, e.g., a CPU or a MPU) in the system or the apparatus to read and execute the program. 
       Example 1 
       [0079]    Hereinafter, example 1 of the present invention will be described. 
       Fundus Imaging Apparatus 
       [0080]    A fundus camera used for fundus imaging in the present example will be described.  FIG. 1  illustrates a schematic diagram of a fundus camera. A digital single-lens reflex camera  50  that can perform imaging at a video rate is connected to a fundus camera body portion  1  via a connection unit  40  as a signal acquisition unit. A perforated mirror  12  is provided on an optical path of an object lens  11  facing an eye (E) to be inspected. On an optical path on the incident side of the perforated mirror  12 , a relay lens  13 , a black point plate  14 , a relay lens  15 , a ring slit plate  16 , a fluorescent exciter filter  17  and a mirror  18  are arranged. Furthermore, on the incident side of the mirror  18 , a condenser lens  19 , a shooting light source  20  including a xenon tube, a condenser lens  21 , and an observation light source  22  including an infrared light emitting diode are arranged. The optical paths in the Figure are indicated by dotted lines. 
         [0081]    Behind the perforated mirror  12 , a focusing lens  24 , a fluorescence barrier filter  25  and an image-forming lens  26  are arranged, and the digital single-lens reflex camera  50  is connected thereto. In the digital single-lens reflex camera  50 , a quick-return mirror  51 , a focal plane shutter (not illustrated) and a two-dimensional sensor  53  are arranged on an optical path that is the same as an optical path behind the object lens  11 . Also, on the reflection side of the quick return mirror  51 , a pentaprism  54  and an ocular lens  55  are provided. Signals received by the two-dimensional sensor  53  are processed in a signal processing board  52 , transferred via a cable to a PC  56  including an HDD  58 , and displayed on a display  57 . In the fundus camera body portion  1 , an internal fixation lamp unit  60  is provided, and light emitted from a light source  61  of an internal fixation lamp is reflected by a dichroic mirror  63  via a lens  62  and applied to the eye to be inspected. A unit for stabilizing fixation is not limited to this, and for example, an external fixation lamp (not illustrated) may be provided. In the fundus camera body portion  1 , a non-illustrated control device is provided, and the control device controls the overall fundus camera while communicating with the PC  56 . 
       Control Method 
       [0082]      FIG. 2  illustrates a functional architecture used in the present example. The functional architecture includes a CPU  203  that controls the overall system, a control device  205  that controls the fundus camera, a fundus camera  201  that acquires fundus images, a display  202  that displays a system status, and an HDD (recording unit)  204  that records, e.g., fundus images and/or imaging conditions. At the time of observation and shooting of a fundus, imaging conditions are provided from the CPU  203  to the control device  205  and a fundus is imaged. After the imaging of the fundus, the image is sent from the fundus camera  201  to the CPU  203  where, e.g., image processing is performed, and then displayed in the display  202  and simultaneously or subsequently stored in the recording unit  204 . 
         [0083]      FIG. 3  illustrates an overall flow of measuring eye movements for a period of time using the above-described functions. A fundus image is acquired using the fundus camera  1  (step  301 ). After the acquisition of the fundus image, a characteristic image (hereinafter referred to as “template image”) is extracted via the PC  56  (step  302 ). The template image and template coordinates, which are reference coordinates of the template image, are stored in the recording unit  204  (step  303 ). Here, template coordinates can be values of center coordinates of a template image where a reference position is an origin (0, 0), and means information on a position of the template image relative to the reference position (first position information). Since the fundus camera successively performs imaging for the period of time, upon acquisition of a following new fundus image (step  304 ), in processing A (step  305 ), the acquired image is searched by the PC  56  for the template image (hereinafter referred to as “template matching”) and the amount of eye movements for the period of time is calculated by processing B (step  306 ). The eye movement amount, the image, measurement time, a real-time monitor image of an anterior eye part, etc., are displayed (step  307 ). The processing from steps  304  to  307  are repeated until the end of the eye movement measurement. 
         [0084]    A detailed flow of template matching in processing A (step  305 ), which is a partial flow, will be described with reference to  FIG. 4 . Here, the template image stored in the recording unit  204  is read (step  401 ), a region for which template matching is performed is set in a newly-acquired fundus image (step  402 ), and template matching is performed in the newly-acquired fundus image (step  403 ). After the end of the template matching, reference coordinates of a matching image, that is, matching coordinates, are stored in the recording unit  204  (step  404 ). Here, matching coordinates are values of center coordinates of the matching image where a point in the second image corresponding to the reference position in the first image is the origin (0, 0), and means information on a position of the matching image relative to the reference position (second position information). Although in the present example, reference coordinates are center coordinates of the matched-object image, any reference coordinates such as upper left corner coordinates may be employed. 
         [0085]    Next, processing B (step  306 ) will be described with reference to  FIG. 5 . In processing B, first, the template coordinates and the matching coordinates are read from the recording unit  204  (step  501 ), the coordinate difference between the template coordinates and the matching coordinates is calculated (step  502 ), and the movement distance is calculated from the coordinate difference. 
       Tracking Measurement: Specific Example 
       [0086]      FIGS. 6A ,  6 B,  6 C,  6 D,  6 E and  6 F illustrate respective images corresponding to the above-described processing. A case where tracking measurement is performed for  20  seconds using the above-described fundus camera, under measurement conditions of acquiring a fundus image with a diameter of 10 mm at a frequency of 10 Hz will be indicated as an example. 
         [0087]      FIG. 6A  illustrates an acquired first fundus image  605 . As illustrated in  FIG. 6A , blood vessels run intricately from a papilla toward an edge portion. After acquisition of the first fundus image, as illustrated by dotted lines in  FIG. 6B , a template image  601  is extracted. Although here, a square image region of 500 μm×500 μm is employed for a template image, a template image is not limited to this and the shape and size of a template image can arbitrarily be determined. The extracted template image  601  and template coordinates Z 0  are stored. In the present example, an origin (0, 0) is set for center coordinates of the fundus image  605 , and center coordinates of the template image of this time is Z 0  (0, −200). The coordinate unit is μm. However, the coordinate setting method is also not limited to this. Next, as illustrated in  FIG. 6C , a region  602  to be searched for the template image  601  when detecting the template image from the new fundus image, that is, a template matching implementing region  602  (hereinafter referred to as “matching region”) is set in the first fundus image by means of the CPU  203 . Here, a matching region in a second image is set with reference to the template coordinates of the template image in the first image (as the center) so that the matching region is broader than a range of movement of the region of the template image resulting from movement or rotation of the eye ball caused by, e.g., involuntary eye movements within measurement time. Next, in  FIG. 6D , illustrating a second fundus image, which is a newly-acquired matching object, an extracting region  603  (first extracting region) is set at a coordinate position that is the same as that of the matching region  602  in the first fundus image. Subsequently, the extracting region  603  is searched for a region corresponding to the template image  601  (template matching). With the above-described configuration, search for a region corresponding to a template can be conducted only in an extracting region, eliminating the need to search the entire second fundus image, and thus, search time can be reduced. As illustrated in  FIG. 6E , after detection of a corresponding region  604 , center coordinates (matching coordinates) Z 1  of the image region  604  corresponding to the template image are measured. In this example, the matching coordinates Z 1  are (0, −400). As illustrated in  FIG. 6F , using the template coordinates Z 0  and the matching coordinates Z 1 , a coordinate change is figured out to calculate the eye movement amount (0 μm, −200 μm in the present example). The above-described template matching in  FIGS. 6D to 6G  is performed for each of newly-acquired fundus images, i.e., matching is performed on each new image acquired at a frequency of 10 Hz, and the amount of movement of the eye ball from the reference position during the measurement time is measured and displayed. 
         [0088]    An example of the matching region setting method performed in  FIG. 6C  is indicated below. In this example, a matching region is a region including a combination of the region of a template image and the region of an area having a fixed width (R 1 ) outward from edge portions of the template image.  FIG. 7B  illustrates an enlarged view  701  of the fundus image  605  in  FIG. 6C .  FIG. 7A  illustrates an enlarged view of a template image  702 .  FIG. 7C  illustrates an enlarged view of a matching region  704 . The matching region  704  is calculated considering the size and precision of the fundus image as well as attribute information such as involuntary eye movements. In the present example, the matching region  704  is a region including the region of an area within R 1  mm outside the template image region from the image edge portions of the template image  702 , and the template image  702 . Here, a value that is larger than the amount of movement of a human eye within the measurement is used for R 1 .  FIG. 8  illustrates the results of measurement of involuntary eye movements of a human eye by means of an apparatus including an internal fixation lamp. As illustrated in  FIG. 8 , in the case of a fixation lamp being provided, movement of a human eye caused by involuntary eye movements tends to fall within a certain distance with a fixation point as the center.  FIG. 9  illustrates a modeling function between time from the start of fixation and distance of movement of a human eye, which includes the aforementioned tendency. The amount of movement of a human eye within measurement time can be figured out based on this graph. For the graph, a known graph provided in advance for each imaging condition, such as external fixation, internal fixation, affected eye or normal subject, age, or time required for capturing one fundus image, can be used, and thus, the graph can arbitrarily be selected depending on the measurement method and/or object. The present example employs measurement for 20 seconds, and according to the function, the amount of movement of the eye ball can be considered 700 μm, and accordingly, R 1  is 700 μm. Thus, the matching region  704  has a size of 1.9 mm×1.9 mm. 
         [0089]    Next, template matching will be described in detail with reference to  FIGS. 10A and 10B . As illustrated in  FIG. 10A , an extracting region  1002  calculated as described above is set in a newly-captured second fundus image  1001  according to the coordinates, and an enlarged view  1002  of the extracting region is searched for a template image. As illustrated in  FIG. 10B , as a result of the search, a corresponding image region  1004  is detected and center coordinates Z 1  thereof are calculated as matching coordinates. The result of the above-described processing may be displayed on the monitor in real time or after measurement. 
         [0090]    As described above, setting a matching region and an extracting region at the time of template matching enables an increase in speed of template matching. Also, as a result of limiting the regions, false detection can be prevented. 
       Example 2 
       [0091]    Example 2 of the present invention will be described below. 
         [0092]    Example 2 will be described in terms of a case where an SLO (scanning laser ophthalmoscope) is used for acquiring fundus images, eye movements are measured from the SLO fundus images by means of a method similar to that of example 1, and the results of measurement of the eye movements are fed back in real time to an optical coherent tomographic imaging apparatus (OCT: optical coherent tomography), thereby providing a high-precision 3D OCT image. 
       OCT Apparatus Configuration 
       [0093]    In the present example, an OCT apparatus is used for an ophthalmologic apparatus. A general description of an OCT apparatus will be given with reference to  FIG. 11 . 
         [0094]    For a low-coherence light source  1101 , an SLD (super luminescent diode) light source or an ASE (amplified spontaneous emission) light source can preferably be used. For low-coherence light, light with wavelengths of around 850 nm and around 1050 nm is preferably used for fundus imaging. In the present example, an SLD light source with a center wavelength of 840 nm and a wavelength half width of 45 nm is used. Low-coherence light applied from the low-coherence light source  1101  enters a fiber coupler  1102  via a fiber and split into a measuring beam (also referred to “OCT beam”) and a reference beam. Although the configuration of an interferometer using a fiber is described here, a spatial optical system with a configuration using a beam splitter may be employed. 
         [0095]    The measuring beam is provided from a fiber collimator  1104  via a fiber  1103  in the form of a collimated beam. Furthermore, the measuring beam passes through an OCT scanner (Y)  1105  and relay lenses  1106  and  1107 , and further through an OCT scanner (X)  1108 , penetrates a dichroic beam splitter  1109 , passes through a scan lens  1110 , a dichroic mirror  1111  and an ocular lens  1112 , and enters an eye to be inspected (e). Here, galvano scanners are used for the OCT scanners (X)  1108  and (Y)  1105 . The measuring beam that has entered the eye e to be inspected is reflected by the retina and returns to the fiber coupler  1102  through the same optical path. The reference beam is guided from the fiber coupler  1102  to a fiber collimator  1113  and provided in the form of a collimated beam. The provided reference beam passes through a dispersion compensation glass  1114  and is reflected by a reference mirror  1116  on an optical length changing stage  1115 . The reference beam reflected by the reference mirror  1116  returns to the fiber coupler  1102  via the same optical path. 
         [0096]    The measuring beam and the reference beam that have returned to the fiber coupler  1102  are combined and guided to a fiber collimator  1117 . Here, light resulting from the combination is called interference light. The fiber collimator  1117 , a grating  1118 , a lens  1119  and a line sensor  1120  are included in a spectroscope. The interference light is measured by the spectroscope in terms of information on the intensity for each wavelength. The information on the intensity for each wavelength measured by the line sensor  1120  is transferred to a non-illustrated PC and reproduced as a tomographic image of the eye to be inspected e. 
       SLO Configuration 
       [0097]    Next, an optical configuration of an SLO imaging unit that acquires fundus images will be described also with reference to  FIG. 11 . For a laser light source  1130 , a semiconductor laser or an SLD light source can preferably be used. There is no restriction on the wavelength to be used as long as a light source that can separate a wavelength to be used, from the wavelengths of the low-coherence light source for OCT, by means of a wavelength separation unit, is used, and a near-infrared wavelength range of 700 nm to 1000 nm is preferably used for the quality of a fundus observation image. In the present example, a semiconductor laser with a wavelength of 760 nm is used. A laser emitted from the laser light source  1130  is output from a fiber collimator  1132  via a fiber  1131  in the form of a collimated beam and enters a cylindrical lens  1133 . Although the present example has been described in terms of a case where a cylindrical lens is used, there is no specific restriction as long as an optical element that can generate a line beam, and a line beam shaper using a Powell lens or a diffraction optical element can be also used. The beam that has been widened by the cylindrical lens  1133  (also referred to as “SLO beam”) is made to pass through a center of a ring mirror  1136  by relay lenses  1134  and  1135 , passes through relay lenses  1137  and  1138  and is guided to an SLO scanner (Y)  1139 . For the SLO scanner (Y), a galvano scanner is used. The beam is further reflected by a dichroic beam splitter  1109 , passes through the scan lens  1110 , the dichroic mirror  1111  and the ocular lens  1112 , and enters the eye e to be inspected. The dichroic beam splitter  1109  is configured so as to transmit an OCT beam and reflect an SLO beam. The SLO beam that has entered the eye to be inspected is applied to the fundus of the eye e to be inspected in the form of a line-shaped beam. The line-shaped beam is reflected or scattered by the fundus of the eye e to be inspected and returns to the ring mirror  1136  via the same optical path. The position of the ring mirror  1136  is conjugate to the position of the pupil of the eye e to be inspected, and thus, light passing through the region around the pupil in the light resulting from backscattering of the line beam applied to the fundus, is reflected by the ring mirror  1136  and forms an image on a line sensor  1151  via a lens  1150 . Based on information on the intensity for each position of the line sensor  1151 , a planar image of the fundus is generated by means of the non-illustrated PC. Although in the present example, an SLO with a line-scan SLO (hereinafter referred to as “L-SLO”) configuration using a line beam has been described, it should be understood that a flying spot SLO may also be used. 
       Internal Fixation Lamp 
       [0098]    The present example includes an internal fixation lamp that makes the eye e to be inspected be fixed thereon to stabilize involuntary eye movements. The internal fixation lamp included in the present example will be described with reference to  FIG. 11  as with the OCT apparatus and the SLO apparatus. For a light source  1170  used for the fixation lamp, a light emitting diode (LED) is used. The position where the light emitting diode is lighted is changed according to the site intended to be imaged, under the control of the PC. The light emitting diode  1170  generates light with a wavelength of 500 nm, and a beam emitted from the light source is applied to the eye e to be inspected via a lens  1171  and the dichroic mirror  1111 . The dichroic mirror  1111 , which is positioned between the scan lens  1110  and the ocular lens  1112 , separates between light with a short wavelength (around 500 nm), and an OCT beam and an SLO beam (no less than 700 nm). 
       Control Method 
       [0099]      FIG. 12  illustrates a functional architecture used in the present example. The functional architecture includes: a CPU  1201  that controls the overall system; respective control devices  1202  and  1203  that control the SLO unit and the OCT unit; a fixation lamp  1208 ; respective cameras  1204  and  1205  that acquire SLO images and OCT images; a display  1206  in a PC, the display  1206  displaying a system status; and a recording unit  1207  in the PC, the recording unit  1207  recording, e.g., fundus images and/or imaging conditions. At the time of imaging a fundus, respective imaging conditions are provided by the CPU  1201  to the control devices  1202  and  1203  and a fundus is imaged. After the fundus being imaged, an image is sent from the camera apparatuses  1204  and  1205  to the CPU  1201 , subjected to image processing and then displayed on the display  1206  and simultaneously or subsequently stored in the recording unit  1207 . 
         [0100]      FIG. 13  illustrates an overall flow of measuring eye movements while acquiring tomographic images of a fundus by means of the OCT unit, using the above-described functions. 
         [0101]    First, processing C (eye movement distance calculation) is performed (step  1301 ), and the width of a matching region is set by the CPU  1201  so that the matching region is broader than a range of movement of the region of a template image resulting from, e.g., involuntary eye movements during measurement time (step  1302 ). Independently from the above processing, the SLO unit is activated and a fundus image is acquired by means of the SLO (step  1303 ). A template image is extracted from the image provided by the SLO (step  1304 ). After the extraction of the template image, the extracted template image and coordinates thereof are stored (step  1305 ). A scan reference position for the OCT unit is recorded (step  1306 ) and the OCT unit&#39;s measurement is started (step  1307 ). After acquisition of a new image from the SLO unit (step  1308 ), as in example 1, processing A (template matching) (step  1309 ) and processing B (eye movement amount calculation) (step  1310 ) are performed, and processing D (feedback to the OCT) is performed (step  1311 ), and the process from steps  1308  to  1311  is repeated while the OCT unit continues measurement of tomographic images (step  1312 ). After the end of the OCT imaging, the measurement of eye movements is terminated (step  1313 ). Processing A and processing B are similar to those in example 1, and thus, a description thereof will be omitted. 
         [0102]    An example of processing C (eye movement distance calculation) (step  1301 ), which is a partial flow, will be described with reference to  FIG. 14 . According to OCT imaging conditions input by a user (step  1401 ), OCT imaging time is measured (step  1402 ). An eye movement distance (matching region) is calculated by applying the OCT imaging time to the graph  901  with reference to  FIG. 9  (step  1403 ). The graph in  FIG. 9  indicates eye movement amount information for a case where a normal eye is measured by an apparatus including an internal fixation lamp. 
         [0103]    Processing D (feedback to the OCT unit) (step  1311 ) will be described with reference to  FIG. 15 . Scan position data for the OCT unit is read by the CPU  1201  (step  1501 ), a voltage to be applied to the OCT scanner is calculated from the eye movement amount (step  1502 ), the power to be applied is transferred to the OCT control device  1203  by means of the CPU  1201  (step  1503 ), and subsequently a signal indicating a shift of the scanners is confirmed (step  1504 ) and then information on the change in scan position is stored (step  1505 ). The change status, the OCT image, the SLO image (with indication of the matching region and the template position), the remaining time, etc., are displayed (step  1506 ). 
       Tracking Measurement: Specific Example 
       [0104]      FIGS. 16A ,  16 B,  16 C,  16 D,  16 E and  16 F illustrate SLO images corresponding to the above-described processing. In the present example, the SLO has a line width of 10 mm and a scan area of 10 mm, that is, a size of an image acquired for the position of the fundus is 10 mm×10 mm. The rate of SLO image acquisition is 30 Hz. Also, the OCT unit makes the camera operate at a rate of 70 k A-scans, a B-scan image (with a fundus scan area of 10 mm and a laser spot diameter of 20 μm) includes 1000 lines, and a 3D image of a retina including 280 B-scan images is acquired. The imaging time amounts to four seconds. 
         [0105]    First,  FIG. 16A  illustrates a fundus image  1601  acquired by the SLO (hereinafter simply referred to as “SLO image”). As illustrated in  FIG. 16A , blood vessels run intricately from a papilla toward the edges. After the acquisition of the first SLO image, the imaging time (four seconds in the present example) is calculated according to the OCT imaging conditions. Referring to  FIG. 9 , an eye ball moves 450 μm in four seconds. During that time, as illustrated in  FIG. 16B , a template image is extracted from the first SLO image  1601 . The template image  1602  and template coordinates X 0  (−25, −200) are stored. The coordinates have (0, 0) at the center of the SLO image. Subsequently, as illustrated in  FIG. 16C , a matching region  1604  is set in consideration of an eye movement distance of 450 μm. Next, as illustrated in  FIG. 16D , an extracting region  1604  is set in a newly acquired second SLO image  1605 . Here, the extracting region in the second image is set with reference to the coordinates of the template image in the first image (as the center) so that the extracting region is broader than a range of movement of the region of the template image resulting from movement or rotation of the eye ball caused by, e.g., involuntary eye movements within the measurement time. Furthermore, the extracting region  1604  is searched for the template image  1602 . As illustrated in  FIG. 16E , after detection of a matching image  1606 , center coordinates X 1  of the matching image  1606  are stored. Subsequently, as illustrated in  FIG. 16F , the distance of movement of the eye ball is calculated from the coordinate difference between the template coordinates X 0  and the matching coordinates X 1 . 
         [0106]    An example of a matching region calculation method will be described with reference to  FIGS. 17A ,  17 B and  17 C. A matching region  1703  includes a region in a SLO fundus image  1706 , the region including a template image  1701  with a matching region setting width R 2  added thereto. As in example 1, an amount of movement of a human eye within measurement time can be figured out based on the graph in  FIG. 9 . For the graph, a known graph provided in advance for each imaging condition, such as external fixation, internal fixation, affected eye or normal subject, age, or time required for capturing one fundus image, can be used, and thus, the graph can arbitrarily be selected depending on the measurement method and/or subject. Among them, in example 2, the matching region setting width R 2  is changed according to the OCT imaging time. Since the OCT imaging time is four seconds, the eye movement distance is 450 μm according to  FIG. 9 . Accordingly, R 2  is 450 μm and the template region  1703  is a region with its respective peripheral sides extended by 450 μm in width compared to the template region  1701 . 
         [0107]    A matching method will be described with reference to  FIGS. 18A and 18B . As illustrated in  FIG. 18A , an extracting region  1802  is set in a new SLO image  1801 , and the extracting region  1802  is searched for a region corresponding to a template image. As illustrated in  FIG. 18B , after detection of a corresponding image region  1804  corresponding to the template image, matching coordinates X 1  are read. 
         [0108]    Here, as illustrated in  FIG. 19 , an OCT image  1901 , eye movement measurement results  1906 , remaining measurement time  1905 , an SLO image (including indication of a matching region and a template image)  1904 , imaging conditions  1908 , etc., may be displayed on a display  1907  in a PC to enable a user to confirm the operation. 
         [0109]    As described above, a matching region and an extracting region are set according to the OCT imaging time, enabling high-speed measurement of eye movements, and consequently, an eye ball can stably be scanned with an OCT beam, enabling acquisition of a 3D image without image displacements caused by eye movements. 
       Example 3 
       [0110]    Example 3 of the present invention will be described. 
         [0111]    As in example 2, example 3 will be described in terms of a case where an SLO (scanning laser ophthalmoscope) is used for acquiring fundus images, eye movements are measured from the SLO fundus images by means of a method that is different from those of examples 1 and 2, and the results of measurement of eye movements are fed back to an optical coherence tomographic imaging apparatus (OCT: optical coherent tomography) in real time at a higher speed, thereby providing a high-precision 3D OCT image. 
         [0112]    The configurations of the fundus imaging apparatus (SLO) and the optical coherence tomographic imaging apparatus (OCT) are similar to those in example 2, and thus, a description thereof will be omitted. A description will be given on the flow, which is a point of difference. 
       Control Flow 
       [0113]      FIG. 20  illustrates a flowchart of an overall control flow of the present example. 
         [0114]    First, an SLO image (first fundus image) is acquired (step  2001 ), a characteristic image is extracted from the acquired SLO image (step  2002 ), and the extracted template image and coordinates (template coordinates) thereof are stored in a recording unit (step  2003 ). A scan reference position of the OCT unit is recorded (step  2004 ), OCT imaging is started (step  2005 ), and simultaneously, an SLO image (second fundus image) is acquired (step  2006 ). Template matching (step  2007 ) in processing E and eye movement amount calculation (step  2008 ) in processing B are performed for the SLO image acquired in step  2006 , and next, as in example 2, processing D (step  2009 ) is performed. After processing D (step  2009 ), an SLO image (third fundus image) is acquired again (step  2010 ), template matching between the template image and the SLO image in processing E (step  2011 ) and eye movement amount calculation for time between the acquisition of the second fundus image and the acquisition of the third fundus image in processing F (step  2012 ) are performed, and also in example 2, reflection of the results in the OCT apparatus in processing D (step  2013 ) is performed. The process from steps  2010  to  2013  is repeated until the end of the OCT imaging. 
         [0115]    The template matching (processing E) in the present example will be described with reference to  FIG. 21 . 
         [0116]    The template image extracted in step  2002  is read (step  2101 ), and the SLO imaging time acquired in step  2001  or  2006  is read (step  2102 ). The amount of movement of an eye ball during imaging is calculated according to  FIG. 9  (under conditions similar to those in example 2) (step  2103 ). As in example 2, an extracting region (second extracting region) is set in the SLO image acquired in step  2006  or  2010 , with the calculated numerical value reflected in the extracting region (step  2104 ). The extracting region setting method is not limited to this, and any method with a matching region set to be broader than a range of movement of the region of the template image within measurement time resulting from movement or rotation of the eye ball caused by involuntary eye movements may be employed. However, in the present example, a matching region is set based on the amount of movement of an eye ball during acquisition of one SLO image, reducing the area subjected to template matching, enabling the processing to be performed for a shorter period of time. Template matching is performed for the region set in step  2105  in the SLO image acquired in step  2006  or  2010  (step  2105 ), and matching information is stored (step  2106 ). 
         [0117]    The eye movement amount calculation (processing F) in the present example will be described with reference to  FIG. 22 . The matching coordinates of the SLO image acquired in step  2011  and the SLO image acquired immediately before that are read (step  2201 ), and the coordinate difference therebetween is calculated (step  2202 ). The movement amount is calculated from the coordinate difference. 
       Specific Example 
       [0118]      FIGS. 23A ,  23 B,  23 C,  23 D,  23 E,  23 F,  23 G and  23 H illustrate respective images corresponding to the above-described processing. As in example 2, the SLO has a line width of 10 mm and a scan area of 10 mm, that is, a size of an image acquired for the position of the fundus is 10 mm×10 mm. The rate of SLO image acquisition is 30 Hz. Also, the OCT apparatus makes a camera operate at a rate of 70 k A-scans, a B-scan image (with a fundus scan area of 10 mm and a laser spot diameter of 20 μm) includes 1000 lines, and a 3D image of a retina including 280 B-scan images is acquired. The imaging time amounts to four seconds. 
         [0119]    First,  FIG. 23A  illustrates a fundus image  2301  (first fundus image) acquired by the SLO. As illustrated in  FIG. 23B , a template image  2305  including blood vessels is extracted from the acquired SLO image  2301 . Center coordinates X0 of the extracted template image are stored. A matching region is set according to the rate of SLO image acquisition, i.e., 30 Hz. Referring to  FIG. 9 , a matching region  2306  is set as illustrated in  FIG. 23C , considering 100 μm, which is the eye movement distance for 1/30 seconds, which are the time required for one SLO image to be acquired. 
         [0120]    Next, as illustrated in  FIG. 23D , a new SLO image  2302  (second fundus image) is acquired. Template matching is performed for an extracting region (first extracting region) in the SLO image  2302 , which corresponds to the matching region  2306  set in  FIG. 23C . A region  2307  corresponding to the template image is detected, and coordinates X1 thereof are acquired. Furthermore, as illustrated in  FIG. 23E , with the reference coordinates (center coordinates in the present example) X1 of the region corresponding to the template image as the center and in consideration of the eye movement distance of 100 μm, a next matching region  2308  is set. Independently from the setting of the matching region, the eye movement amount is calculated from the coordinate values of coordinates X0 and X1. The calculation results are fed back to scanners in the OCT apparatus. 
         [0121]    As illustrated in  FIG. 23F , a new SLO image  2303  is acquired. Template matching is performed for an extracting region (second extracting region) in the SLO image  2303 , which corresponds to the matching region  2308  set in  FIG. 23E . A region  2309  corresponding to the template image is detected, and reference coordinates (center coordinates in the present example) X2 of the region corresponding to the template image are acquired. Furthermore, as illustrated in  FIG. 23G , with the coordinates X2 as the center and in consideration of the eye movement distance of 100 μm, a next matching region  2310  is set. Independently from the setting of the matching region, the eye movement amount is calculated from the coordinate values of the coordinates X1 and X2. 
         [0122]    Although a description of the subsequent processing will be omitted, processing for further acquiring a new SLO image  2304  as illustrated in  FIG. 23H , performing template matching for a region in the SLO image  2304  corresponding to the matching region  2310 , detecting a region corresponding to the template image, acquiring reference coordinates thereof, and setting a new matching region is repeated until the end of the OCT imaging. 
         [0123]    As described above, a matching region is set for each acquired SLO image according to the rate of SLO image acquisition, and applied to an SLO image acquired next, enhancing the feedback speed. Furthermore, during OCT imaging, the amount of movement of an eye ball is calculated from the SLO images and fed back to the OCT apparatus, thereby acquiring an OCT image while moving a region scanned with OCT scan according to the eye movement amount, enabling provision of a high-quality OCT image. 
         [0124]    Although in the preceding examples, one characteristic image is extracted to figure out a movement amount, it is possible that: a plurality of characteristic images is extracted, and an average value of respective movement amounts obtained as a result of pattern matching being performed based on the respective characteristic images is fed back to the OCT apparatus. 
       Example 4 
       [0125]    Example 4 of the present invention will be described. 
       Control Method 
       [0126]    A fundus camera is used for fundus image acquisition. The configuration and functional architecture of a used apparatus are similar to those in example 1, and thus, an overlapping description thereof will be omitted. 
         [0127]      FIG. 24  illustrates an overall flow of measuring eye movements for a fixed period of time using the above-described functions. A first fundus image is acquired using a fundus camera  1  (step  2401 ). After the acquisition of the first fundus image, before extraction of a characteristic image (template), a region having a fixed width from a peripheral portion of the first image toward center coordinates of the image is set as a masked region (first region) (step  2402 ). Here, the masked region is set so that the width of the masked region is broader than a distance in which an eye ball moves as a result of, e.g., involuntary eye movements within measurement time. With the portion other than the masked region as an extracting region (second region), a template image is extracted from the extracting region (step  2403 ). There is no specific limitation on the method for extracting a template image as long as the method is a characteristic image extraction method enabling search and detection between a plurality of images. The template image, information on a reference position set in the first image, and information on template coordinates are stored in a recording unit  204  (step  2404 ). Since the fundus camera performs imaging successively for the fixed period of time, a following new fundus image (second fundus image) is acquired (step  2405 ). In processing G (step  2406 ), the entire acquired second fundus image is searched for the template image (template matching). In processing B, a position change of the template image is calculated and the eye movement amount for the fixed period of time is calculated (step  2407 ). The above-described processing from steps  2405  to  2407  is repeated until the end of measurement of eye movements (a new image is acquired). Also, the eye movement amount, the image, the measurement time and real-time monitor image of an anterior eye part, etc., may be displayed on a display  202  (step  408 ). 
         [0128]    Processing G (step  2406 ), which is a partial flow, will be described with reference to  FIG. 25 . In template matching, the template image stored in the recording unit  204  is read (step  2501 ) and template matching is performed for a newly-acquired fundus image (step  2502 ). A method to be employed for template matching is not limited and the template matching may also be performed by means of any known method. After the template matching (step  2503 ), matching coordinates are stored in the recording unit  204 . Since processing B is similar to that in example 1, an overlapped description thereof will be omitted. 
       Tracking Measurement: Specific Example 
       [0129]      FIGS. 26A ,  26 B,  26 C,  26 D,  26 E,  26 F and  26 G illustrate a specific example in which the respective processing steps described above are performed for acquired fundus images. Using the above-described fundus camera  1 , tracking measurement is performed on a fundus for ten seconds under measurement conditions of acquiring a fundus image with a diameter of 10 mm at a frequency of 10 Hz. 
         [0130]    First,  FIG. 26A  illustrates an acquired first digital fundus image. As illustrated in  FIG. 26A , blood vessels run intricately from a papilla  2601  toward an edge portion. After the acquisition of the first fundus image, as illustrated in  FIG. 26B , a region of Y=600 μm from the edge portion of the image is set as a masked region  2602 . A method for setting the width Y of the masked region will be described later. A template image  2603 , which is indicated by dotted lines in  FIG. 26C , is extracted from an extracting region other than the masked region with arrangement made not to extract a template from the masked region  2602 . Although here, a template image has a square shape with a size of 500 μm×500 μm, the template image is not limited to this and the shape and size of the template image can arbitrarily be determined. In the present example, where center coordinates of a fundus photo is an origin (0, 0), center coordinates (template coordinates) of the template image extracted from the first fundus image, which are illustrated in  FIG. 26D , are Z 0  (0, −200). Here, the coordinate unit is μm. Next,  FIG. 26E , which is an object second fundus image, is searched for the template. As illustrated in  FIG. 26F , after template matching being performed, matching coordinates Z 1  of a matching image  2605  corresponding to the template image are measured. In this example, the matching coordinates Z 1  are (0, −400). As illustrated in  FIG. 26G , a coordinate change is figured out from the template coordinates Z 0  and the matching coordinates Z 1  and the eye movement amount ((0 μm, −200 μm) in the present example) is calculated. The above-described template matching in  FIGS. 26E to 26G  is repeated in a manner similar to the above for third, fourth and onward fundus image figures acquired at 10 Hz, and the amount of movement of the eye ball from a reference position during measurement is measured and displayed. 
         [0131]    An example of the method for setting a masked region, which has been performed in  FIG. 26B , will be described below. As illustrated in  FIG. 26B , in consideration of the size and precision of fundus images and attribute information such as a distance in which an eye ball moves as result of involuntary eye movements within measurement time during which all the fundus images are captured (ten seconds in the present example), an area having a width of Y mm from the peripheral portion of the image is designated as a masked region. In this case, the diameter of the image is 10 mm and the precision is around 10 μm. Also, an amount of movement of a human eye within measurement time can be figured out according to the graph in  FIG. 9 . For this graph, a known graph provided in advance for each imaging condition, such as external fixation, internal fixation, affected eye or normal subject, age, or time required for capturing one fundus image, can be used, and thus, the graph can arbitrary be selected depending on the measurement method and/or object. Involuntary eye movements of a normal subject using the present external fixation lamp amount to around 600 μm in measurement time of ten seconds. Accordingly, under the aforementioned conditions, a region with a width of 600 μm from the peripheral portion of the image has been secured as a masked region. 
         [0132]    This masked region is a region that may fall outside the measurement area depending on the movement of the human eye during measurement. Accordingly, as described above, a masked region  202  is set when a template is extracted, avoiding the template image from falling outside the area of an acquired image due to movements of a human eye during measurement, preventing a template detection error and enabling stable measurement of eye movements. 
       Example 5 
       [0133]    Example 5 of the present invention will be described below. 
         [0134]    Example 5 will be described in terms of a case where an SLO is used for fundus image acquisition, eye movements are measured from the SLO fundus images by means of a method similar to that of example 4, and the results of measurement of the eye movements are fed back in real time to an OCT apparatus, thereby providing a high-precision stereoscopic OCT image. 
         [0135]    The configuration and functional architecture of an ophthalmologic apparatus used in the present example is similar to those of example 2, and thus, an overlapping description thereof will be omitted. 
         [0136]      FIG. 27  illustrates an overall flow of measuring eye movements during acquiring tomographic images of an eye ball by means of an OCT apparatus using the above-described functions. 
         [0137]    First, processing C (eye movement distance calculation) is performed (step  2701 ), and the width of a masked region is determined so as to be broader than a distance in which the eye ball moves as a result of, e.g., involuntary eye movements within measurement time (step  2702 ). Independently from the above processing, an SLO apparatus is activated and a fundus image is acquired by means of the SLO (step  2703 ). For the SLO image, a masked region (first region) is set from a peripheral portion of the first image toward center coordinates of the image (step  2704 ), and a template is extracted from an extracting region (second region), which is a region other than the masked region (step  2705 ). After the extraction of the template, the image, which is template information, and template coordinates, which are center coordinates of the template image, are stored (step  2706 ). A scan reference position of the OCT apparatus is stored (step  2707 ) and measurement by means of the OCT apparatus is started (step  2708 ). After acquisition of a new image from the SLO apparatus (step  2709 ), as in example 4, processing G (template matching) (step  2710 ) and processing B (eye movement amount calculation) are performed (step  2711 ), processing D (feedback of the eye movement amount to the OCT) is performed (step  2712 ), and during the OCT apparatus continuing measuring tomographic images, the process from steps  2709  to  2712  is repeated (step  2713 ). 
         [0138]    After the end of OCT imaging, the measurement of eye movements is terminated (step  2714 ). Processing G and processing B are similar to those in example 4, and thus, a description thereof will be omitted. 
         [0139]    Processing C (eye movement distance calculation: step  2701 ), which is a partial flow, is similar to processing C in example 2, and thus, an overlapping description will be omitted. 
         [0140]    Processing D (feedback to the OCT apparatus: step  2712 ) is also similar to processing D in example 2, and thus, an overlapping description will be omitted. In the present example, a matching region may be displayed instead of displaying a masked region in step  1506 . 
       Tracking Measurement: Specific Example 
       [0141]      FIGS. 28A ,  28 B,  28 C,  28 D,  28 E,  28 F and  28 G illustrate a specific example in which the respective above-described processing steps are performed for SLO images acquired when measuring a normal eye using an apparatus including an internal fixation lamp. The L-SLO has a line width of 10 mm and a scan range of 10 mm, that is, a size of an image for a fundus position is 10 mm×10 mm. SLO images can be acquired at a frequency of 30 Hz. For conditions for acquiring OCT images, the above-described SD-OCT is used, a camera is made to operate at a rate of 70 k A-scans, a B-scan image (with a fundus scan range of 10 mm, and a laser spot diameter of 20 μm) includes 1000 lines, and a 3D image of a retina including 280 B-scan images is acquired. The measurement time amounts to four seconds. 
         [0142]      FIG. 28A  illustrates a fundus image (SLO image) acquired in the SLO. As illustrated in  FIG. 28A , blood vessels run intricately from a papilla toward an edge portion. After the acquisition of the first SLO image, the measurement time (four seconds in the present example) is calculated from the OCT imaging conditions. Referring to  FIG. 9 , an eye ball moves 470 μm in four seconds, and thus, as illustrated in  FIG. 28B , a region of P=470 μm from the edge portion of the image is set in a masked region  2801  and a template image  2802  with a size of 500 μm×500 μm such as indicated by dotted lines in  FIG. 28C  is extracted with arrangement made not to extract a template from the masked region  2801 . Although here, the template image has a square shape with a size of 500 μm×500 μm, the template image according to the present invention is not limited to this, and the shape and size of the template image may arbitrarily be determined. After the extraction of the template, template coordinates X 0  in  FIG. 28D  is set as a reference for movement amount calculation. In the present example, where center coordinates of the SLO image is an origin (0, 0), template coordinates of this template image were X 0  (−50, −200). The coordinate unit is μm. Next, template matching is performed for  FIG. 28E , which is a next object second fundus image. As illustrated in  FIG. 28F , after the template matching being performed, matching coordinates X 1  of a matched matching image are measured. In this second fundus image figure, the matching coordinates were X 1  (−50, −400). As illustrated in  FIG. 28G , coordinate changes are obtained from the template coordinates X 0  and the matching coordinates X 1  to calculate the eye movement amount (0 μm, −200 μm). The results of the above-described calculation are reflected in scanners  1105  and  1108  in the OCT apparatus via a CPU, whereby the scan position of the OCT is changed. The above-described template matching from  FIGS. 28E to 28G  is repeated: matching is performed for each of SLO images acquired at a frequency of 30 Hz, and fed back to the OCT apparatus. In the present example, during the above processing, as illustrated in  FIG. 19 , an OCT image  1901 , eye movement measurement results  1906 , remaining measurement time  1905 , an SLO image (including indication of a masked region and a template image)  1904 , imaging conditions  1908 , etc., may be displayed on a display  1907  to enable a user to confirm the operation. 
         [0143]    A method for setting a masked region such as illustrated in  FIG. 28B  will be described below. Inspection time for inspection using an ophthalmologic apparatus (the OCT apparatus in the present example) that is different from a fundus imaging unit is calculated. From the inspection time and the imaging conditions, a graph in  FIG. 9  matching the imaging conditions, for example, external fixation, internal fixation, affected eye or normal subject, age, or time required for capturing one fundus image, is selected, and from the selected graph, the amount of movement of the eye ball is calculated. A masked region is determined with the amount of movement of the eye ball made to be the width of the masked region P. 
         [0144]    This masked region is a region that may fall outside a measurement area due to movements of a human eye during measurement. Accordingly, setting a masked region  2802  for template extraction according to the OCT imaging time as described above avoids a template image from falling outside the area of an acquired image due to movements of a human eye during measurement, preventing a template detection error and enabling provision of a stereoscopic OCT image without image displacements caused by eye movements. 
       Example 6 
       [0145]    In examples 4 and 5, a region where a masked region is set is not limited to a peripheral portion of an image, and a masked region may be set in, for example, a center portion of an acquired first fundus image. 
         [0146]    Here, a masked region may be set in only a center portion of an image, or may also be set in both of a peripheral portion and a central portion of an image. 
         [0147]    For detecting a rotation of an eye ball, a plurality of characteristic images may be extracted from a first fundus image as illustrated in  FIG. 29 . Here, as in examples 4 and 5, a masked region (first region) is set so as to extend from a peripheral portion of the image as indicated by a shaded portion  2905 . Furthermore, a masked region is also set in a center portion  2906  of the image. Here, the center portion can be defined as, for example, a region defined by a circle with a center of the first fundus image as its center, the region having a diameter that is larger than a distance in which an eye ball moves during the time of imaging all fundus images. In the present example, a radius of the circuit is set to be a value that is larger than the amount of movement of an eye ball because when the respective characteristic images move over the center, it is difficult to distinguish among a rotation movement, a shift of an eye ball and a magnification of the eye ball (movement in the eye axis direction) from each other. Even if the radius is smaller than the amount of movement of the eye ball, an adjustment can be made by means of measurement conditions (changing the number and/or size of characteristic images, and/or the method for extraction of characteristic images). As in examples 4 and 5, a distance in which an eye ball moves can be figured out from a graph indicating a function modeling a relationship between time from the start of fixation and an amount of movement of a human eye. Also, similarly, a known graph provided in advance for each imaging condition, such as external fixation, internal fixation, affected eye or normal subject, age, or time required for capturing one fundus image, can be used, and thus, the graph can arbitrary be selected depending on the measurement method and/or subject. 
         [0148]    If a characteristic image is extracted from the masked region, no coordinate difference due to the rotation of the eye ball may be caused between template coordinates and matching coordinates, disabling measurement of the rotation. Accordingly, setting the masked region  2906  for extracting the template according to the imaging time as described above can prevent extraction of a template from the center portion of the image, enabling more reliable detection of a rotation movement of the eye ball. 
         [0149]    Then, after the determination of the mask, the image is divided into four parts as indicated by A, B, C and D, and characteristic images  2901 ,  2902 ,  2903  and  2904  are extracted from respective areas resulting from the division. Subsequently, as in examples 1 and 2, distances of movements of the respective characteristic images  2901 ,  2902 ,  2903  and  2904  are detected and the rotation of the eye ball is calculated from the four points. 
         [0150]    As described above, extraction of characteristic images from an image other than enables correct calculation of a rotation of an eye ball. Although in the present example, a circular image as in example 1 has been used, similar processing can be performed with a rectangular fundus image as in example 2. 
       Others 
       [0151]    Although the respective examples have individually been described, two or more of the examples may be combined (for example, a matching region and a masked region may be set at the same time). 
         [0152]    Although in each of the above examples, extraction is performed using a template image of blood vessels, an effect similar to those in the examples can be obtained using a template image of a macula or a papilla. For fundus image acquisition, fundus images may be acquired using an imaging system other than those used in the examples, such as a fundus camera, a scan laser ophthalmoscope (SLO) and an optical coherence tomographic imaging apparatus. Furthermore, an effect similar to those in the examples can be provided using, e.g., a visual field test apparatus for conducting a visual field test. 
         [0153]    Furthermore, in the case of using, e.g., blood vessels, in order to detect a rotation of an eye ball, a plurality of characteristic images may be extracted. In such case, also, an effect similar to those in the examples can be provided by setting a matching region and an extracting region for each of the characteristic images. 
         [0154]    The sequences in the flows indicated in examples 1 to 6 are not limited to these, and an effect similar to those provided by the present invention can be provided even if a sequence different from those in examples 1 to 6 is provided or another flow is provided. Also, although center coordinates are used for reference coordinates for calculating a movement amount, an effect similar to that case can be provided using edge coordinates or coordinates in crossing of blood vessels. 
         [0155]    Although in examples 1 and 4, a matching region is set to 700 μm and a masked region is set to 600 μm because an internal fixation lamp is provided, a width set for a matching region/masked region can arbitrarily be set according to various circumstances such as imaging conditions for fundus images. For example, where fixation is more stable, a matching region/masked region can be reduced in size, while where fixation is unstable as in the case of, e.g., an older person or an affected eye, a matching region/masked region can favorably be increased in size. Also, as an imaging condition, time required for capturing one fundus image may be taken into consideration. Where measurement is performed a plurality of times for a same test object, a more accurate matching region/masked region can be set by using measurement data in the previous measurements, enabling an increase in speed of template matching. Although in the present example, a region obtained as a result of extending all the peripheral sides of a template image by a same value R 1  is set as a matching region, an effect similar to those in the examples can be provided if values differing depending on the extension directions are employed according to the amount of movement of a human eye during measurement time. Similarly, the width of the masked region may have different values in the respective directions according to the amount of movement of a human eye. 
         [0156]    Although in examples 1 to 6, corrections for eye movements have been made in real time for an ophthalmologic apparatus, an effect is also exerted where correction or post-processing is performed on an acquired image after the end of measurement. 
         [0157]    For the graph in  FIG. 9  for calculating an eye movement amount, a more accurate movement amount can be calculated by using a different graph depending on the conditions, such as external fixation/internal fixation, affected eye/normal subject, age, individual, enabling provision of a more accurate matching region and mask region. 
       Other Embodiments 
       [0158]    Aspects of the present invention can also be realized by a computer of a system or apparatus (or devices such as a CPU or MPU) that reads out and executes a program recorded on a memory device to perform the functions of the above-described embodiment(s), and by a method, the steps of which are performed by a computer of a system or apparatus by, for example, reading out and executing a program recorded on a memory device to perform the functions of the above-described embodiment(s). For this purpose, the program is provided to the computer for example via a network or from a recording medium of various types serving as the memory device (e.g., computer-readable medium). 
       REFERENCE SIGNS LIST  
       [0000]    
       
           1 : fundus camera body portion 
           50 : digital single-lens reflex camera 
           602 : matching region 
         R 1 : matching region setting width 
           1002 : extracting region 
           2602 : masked region 
           2802 : masked region 
         Y: masked region setting width 
           2906 : masked region 
       
     
         [0168]    This application claims the benefit of Japanese Patent Applications No. 2010-056545, filed Mar. 12, 2010, No. 2010-056557, filed Mar. 12, 2010, and No. 2010-243537, filed Oct. 29, 2010, which are hereby incorporated by reference herein in their entirety.