Image analysis apparatus, image processing apparatus, and image analysis method

An image analysis apparatus includes a moving image input unit which accepts an input of a moving image of a subject irradiated with X-rays, a determination unit which analyzes the previous frame and current frame of the moving image, and determines based on the analysis result whether or not any of a change in relative position between an exposure field of the X-rays and an observation portion of the subject, a change in imaging condition of the moving image, and a change in observation portion of the subject is detected, and a feature amount setting unit which sets feature amounts extracted from the current frame in the current frame when the determination unit determines that any of the changes is detected, and sets feature amounts set in the previous frame in the current frame when the determination unit determines that no change is detected.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing technique that inputs a moving image and corrects the moving image and, more particularly, to an image analysis technique and image processing technique which extract feature amounts of a moving image.

2. Description of the Related Art

Conventionally, various image correction techniques have been proposed to improve image data. Feature amounts of a moving image may include color, luminance, tone, brightness, hue and chroma of that image data. An image correction technique may, for example, extract these feature amounts from an image and apply tone correction, sharpness correction, color balance correction, white balance correction, and exposure correction to the image data based on the extracted feature amounts.

The aforementioned image correction technique can be applied to image correction of not only a still image but also a moving image. By applying the aforementioned corrections to each frame (to be referred to as a “frame image” hereinafter) that forms a moving image, the image quality can be corrected.

However, when the aforementioned image correction technique is applied to a moving image, since it executes correction processing for each frame, correction values of a current frame may often be different from those of the previous and next frames, and these correction value differences may be recognized as image quality variations. In particular, when a moving image has a small motion from one frame to the next, the image correction may be excessively applied due to errors of feature amounts, and such correction result may be recognized as deterioration. As a method of solving this problem, a method of smoothing feature amounts extracted from the current frame image and those extracted from the previous frame image, and setting the smoothed feature amounts as those of the current frame, is possible. However, with this method, when a large change occurs between frames, feature amounts cannot be changed to a large enough extent, resulting in a poor result.

As a method of solving the problem regarding this poor result, a method to correct the feature amounts of the current frame image and set the corrected feature amounts as those of both the previous and next frame images has been proposed (Japanese Patent Laid-Open No. 2005-269542). More specifically, tentative feature amounts are calculated and are adjusted according to a result of a cut point analysis unit to set them as feature amounts.

However, when feature amounts of a moving image are to be calculated, as described above, often they may be different for different (e.g. the prior and future) frames. In this case, even when the tentative feature amounts of the current frame are adjusted as in the conventional method, a substantial influence by errors are included without being wanted, and a stable image quality cannot be obtained. Moreover, with the conventional method, since a scene change of a moving image is discriminated by analyzing variations of individual images, the precision of discrimination is low. Furthermore, with the conventional method, since tentative feature amounts are calculated for all frames, a heavy load is imposed on arithmetic processing.

SUMMARY OF THE INVENTION

Hence, it is desirable to provide an image analysis technique and image processing technique, which suppress the influence of feature amount variations, assure a quick response to image changes, can reduce the load on the arithmetic processing, and can realize stable image quality.

The present invention in its first aspect provides an image analysis apparatus comprising: a moving image input unit adapted to receive data representing a moving image of a subject irradiated with X-rays; a determination unit adapted to analyze a previous frame and a current frame of the moving image, and to determine based on the analysis result whether or not any of: a change in relative position between an exposure field of the X-rays and an observation portion of the subject, a change in imaging condition of the moving image, and a change in observation portion of the subject is detected; and a feature amount setting unit adapted to set a feature amount extracted from the current frame in the current frame when the determination unit determines that any of the changes is detected, and to set a feature amount extracted from the previous frame in the current frame when the determination unit determines that no change is detected.

The present invention in its second aspect provides an image processing apparatus comprising: an image analysis apparatus comprising: a moving image input unit adapted to receive data representing a moving image of a subject irradiated with X-rays; a determination unit adapted to analyze a previous frame and a current frame of the moving image, and to determine based on the analysis result whether or not any of: a change in relative position between an exposure field of the X-rays and an observation portion of the subject, a change in imaging condition of the moving image, and a change in observation portion of the subject is detected; and a feature amount setting unit adapted to set a feature amount extracted from the current frame in the current frame when the determination unit determines that any of the changes is detected, and to set a feature amount extracted from the previous frame in the current frame when the determination unit determines that no change is detected, and an image processing unit adapted to perform image processing based on a feature amount set by the image analysis apparatus.

The present invention in its third aspect provides an image analysis method executed in an image analysis apparatus which comprises a moving image input unit adapted to receive data representing a moving image of a subject irradiated with X-rays, the method comprising: a determination step of analyzing a previous frame and a current frame of the moving image, and determining based on the analysis result whether or not any of a change in relative position between an exposure field of the X-rays and an observation portion of the subject, a change in imaging condition of the moving image, and a change in observation portion of the subject is detected; and a feature amount setting step of setting a feature amount extracted from the current frame in the current frame when it is determined in determination step that any of the changes is detected, and setting a feature amount extracted from the previous frame in the current frame when it is determined in determination step that no change is detected.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will be exemplarily described in detail hereinafter with reference to the drawings. Components described in these embodiments are merely examples, and the technical scope of the present invention is defined by the scope of the claims and is not limited by the following individual embodiments.

First Embodiment

A schematic arrangement of an image processing apparatus and the process it performs according to the first embodiment will be described below with reference to the block diagram shown inFIG. 1and the flowchart shown inFIG. 2.

An image input unit101serves as a moving image input unit, and receives an external moving image input. An irradiation device102controls the irradiation of a subject (also referred to as an examinee), and outputs its imaging condition information and position information. A bed103holds a subject during examination, and outputs its position information. A gantry104holds an X-ray sensor, and outputs its position information and a frame rate of the sensor. A biological information monitor105monitors biological information of the subject (e.g. a patient/examinee), and outputs that biological information. A change determination unit106receives information from the image input unit101, irradiation device102, bed103, gantry104, and biological information monitor105. Then, the change determination unit106determines the detection and non-detection of changes, i.e., whether or not a large change in relative position between an observation portion (i.e. a portion of the subject that is observed), the irradiation device, and the X-ray sensor is detected; or whether a large change in imaging condition or a large change in observation portion are detected. The conditions that are required for a “large” change to be determined are listed later in this document. A “large” change is simply a change worthy of being noted and may be any change at all or a change larger than a predefined threshold, as will be discussed below. A feature amount setting unit107sets feature amounts of an image based on the change determination result received from the change determination unit106. An image processing unit108executes image processing based on the feature amounts set by the feature amount setting unit107.

The processing sequence of the image processing apparatus will be described below with reference toFIG. 2. In step S201, the image input unit101accepts an input of a current frame image (i.e. data representing the image that is present in a current frame of video data). In step S202, the change determination unit106receives the input image from the image input unit101, imaging condition information and X-ray tube position information input from the irradiation device102, and bed position information input from the bed103. The change determination unit106also receives sensor position information input from the gantry104and biological information input from the biological information monitor105. Then, the change determination unit106determines whether or not a (large) change in relative position between an observation portion and exposure field, a (large) change in imaging condition, and/or a (large) change in observation portion are detected. Details of the change determination method by the change determination unit106will be described later.

Note that the change determination method by the change determination unit106is not limited to a method which receives information from the image input unit101, irradiation device102, bed103, gantry104, and biological information monitor105, and determines changes. For example, a method that receives information from only the irradiation device102, bed103, and gantry104and determines changes therefrom may be used. Alternatively, any other methods of determining whether or not at least one of a change in relative position of the X-ray sensor, a change in imaging condition, and a change in observation portion is detected may be applied.

If the change determination unit106determines that changes are detected, the feature amount setting unit107performs image analysis processing, and extracts feature amounts of the current frame (S203). If the change determination unit106determines that no change is detected, the feature amount setting unit107extracts feature amounts set in the previous frame (S204).

In step S205, the feature amount setting unit107sets the extracted feature amounts. Details of the feature amount setting method by the feature amount setting unit107will be described later. In step S206, the image processing unit108performs image processing such as tone conversion processing, sharpening processing, and noise suppression processing based on the feature amounts set by the feature amount setting unit107, and outputs the processed image. Details of the image processing method by the image processing unit108will be described later. Once the aforementioned processes in steps S201to S206are executed, a series of processes in the current frame of a moving image ends.

Conventionally, when image correction techniques—that apply corrections to each frame—have been applied to moving image data, correction values used for a current frame have often been different from those used in previous and following frames because each frame was analyzed and corrected independently. The visible effect of this arises as image quality variations. Furthermore, as feature amounts have been calculated for all frames in the conventional methods, a heavy load is imposed on the arithmetic processing of the image data. On the other hand, the present embodiment employs a feature amount setting means (and method)107that minimizes the processing load by using the output of the determination means. If the determination means determines that any changes have been detected (as will be described below), a feature amount of the current image will ideally be obtained in order to suppress image quality variations. If there have been no changes, on the other hand, the same feature amount as the previous (unchanged) image can be used, and is used to save processing time. In this latter case, because the image quality variations are small, the feature amount extracted from the previous image available as the feature amount of the current frame without having to calculate the feature amount of the current frame and the processing load is reduced.

The change determination processing by the change determination unit106will be described in detail below with reference to the block diagram shown inFIG. 3and the flowchart shown inFIG. 4.FIG. 3is a block diagram showing the arrangement of the change determination unit106in detail.

As mentioned above, feature amounts may include characteristics of an image such as color, luminance, tone, brightness, hue and chroma. The feature amount corresponding to a frame constituting a moving image can be obtained by analyzing the frame and detecting these characteristics. The feature amounts between frames that constitute a moving image are changed in accordance with a change in relative position between an exposure field of the X-rays and an observation portion of the subject, a change in imaging condition of the moving image, and a change in the observed portion of the subject.

The change determination unit106determines the detection or non-detection of at least one of the changes listed above. When the determination unit determines that any of the changes is or has been detected, the current frame feature amount extraction unit1204included in the feature amount setting unit107extracts a current feature amount from the current frame and outputs this extracted current feature amount to the feature amount storage unit1203. In the feature amount storage unit1203, the current feature amount is stored (or set) in such a way so as to correspond to the frame image from which the current feature amount is extracted. In this way, the feature amount is available for the next frame image if no change is determined as detected by the determination unit between the current frame image and the next frame image.

An image variation analysis unit301receives the current frame image from the image input unit101, and analyzes a degree of variation of the current frame image from the previous frame image.

The image variation analysis unit301includes an image analysis unit302, analysis value comparison unit303, and analysis value storage unit304. The image analysis unit302analyzes the current frame image, and extracts analysis values. The analysis value comparison unit303compares the analysis values of the current frame image with those of the previous frame image, and calculates a comparison result. The analysis value storage unit304stores the analysis values of the current frame image and those of the previous frame image. The analysis value of the current frame image is stored for comparison with the subsequent frame image. During the subsequent frame image analysis, therefore, the current frame image becomes a previous frame image.

A position change analysis unit311receives position information of the X-ray tube in the irradiation device from the irradiation device102, bed position information from the bed103, and X-ray sensor position information from the gantry104, and determines whether or not a relative position between an observation portion (i.e. the portion of subject being observed or imaged) and an exposure field (portion of subject being irradiated) has changed. The position change analysis unit311includes a position storage unit312and position calculation unit313. The position storage unit312stores pieces of position information regarding the X-ray tube, the bed and the X-ray sensor at acquisition timings of the current frame image and previous frame image. The position calculation unit313calculates a change in relative positional relationship between the observation portion and exposure field based on the pieces of position information of the X-ray tube, bed, and X-ray sensor at the acquisition timings of the current frame image and previous frame image.

An imaging condition change determination unit321receives X-ray generation conditions such as a tube voltage, tube current, and irradiation time from the irradiation device102, and the sensor frame rate from the gantry104, and determines whether or not the imaging conditions have changed. In other words, imaging conditions include tube voltage, tube current, irradiation time and sensor frame rate, as well as other conditions that may be derivable from such values, such as X-ray tube power and amount of radiation detected per frame.

The imaging condition change determination unit321includes an imaging condition storage unit322and imaging condition comparison unit323. The imaging condition comparison unit323compares the tube voltages, tube currents, irradiation times, and frame rates at the acquisition timings of the current frame image and previous frame image, and calculates the comparison result (i.e. a comparison of the imaging conditions between the current frame and the previous frame to determine whether or not they have changed and outputting the result of the determination as the comparison result). The imaging condition storage unit322stores tube voltages, tube currents, irradiation times, and frame rates at the acquisition timings of the current frame image and previous frame image.

A biological information variation analysis unit331receives biological information such as heart rate information and respiration phase information from the biological information monitor105, and analyzes a degree of variation of the biological information at the acquisition timing of the current frame image with respect to the biological information at the acquisition timing of the previous frame image.

The biological information variation analysis unit331includes a biological information comparison unit332and biological information storage unit333. The biological information comparison unit332compares the biological information at the acquisition timing of the current frame image and the biological information at the acquisition timing of the previous frame image, and calculates their comparison result. The biological information storage unit333stores the biological information at the acquisition timing of the current frame image and at the acquisition timing of the previous frame image.

A total change determination unit341receives the image variation analysis result from the image variation analysis unit301, the position change determination result from the position change determination unit311, the imaging condition determination result from the imaging condition change determination unit321, and the biological information variation analysis result from the biological information variation analysis unit331. Then, the total change determination unit341determines whether or not a large change in relative position between the observation portion and exposure field and a large change in imaging condition are detected.

A series of processes of the change determination method executed by the change determination unit106will be described below with reference toFIG. 4. Steps S401to S406, S411to S413, S421to S423, and S431to S433of this flowchart operate in parallel, and steps S441and S442are executed at the time of completion of these processes.

Steps S401to S406executed by the image variation analysis unit301will be described first. This embodiment will describe in detail a case in which the current frame is not the first frame at the beginning of imaging. When the current frame is the first frame at the beginning of imaging, the total change determination unit341cannot determine that any change has been detected, as there is no previous frame with which to compare the current frame. Nevertheless, in the case of the first frame, the change determination unit341outputs the result “change detected” to the feature amount setting unit107so that the feature amount setting unit107treats the current frame as a frame for which the current feature amount must be extracted and used.

The image analysis unit302in the image variation analysis unit301receives the current frame image (S401). The image analysis unit302recognizes an exposure field using the current frame image, and discriminates a portion irradiated with X-rays and other portions in the sensor (S402). Various methods about exposure field recognition have been proposed. For example, methods proposed by Japanese Patent Laid-Open No. 2000-271107, Japanese Patent Laid-Open No. 2003-33968, and the like may be used.

The image analysis unit302generates a histogram indicating the distribution of image (i.e. pixel output frequency) values in the exposure field (S403). The image analysis unit302analyzes the generated histogram and calculates analysis values (S404). An example of histogram analysis will be described below usingFIGS. 5A to 5C.FIG. 5Ashows the current frame image. This image has the number of tones=4096 and a size=100×100 pixels (or other appropriate image units) in the exposure field. The image analysis unit302generates a histogram in the exposure field of this image (FIG. 5B). In other words, the histogram shown inFIG. 5Bhas in its x-axis pixel value within the exposure field and frequency output of each pixel in the y-axis. The image analysis unit302generates an accumulated (i.e. displaying cumulative pixel frequency in the y-axis) histogram (FIG. 5C) from the histogram ofFIG. 5B, and calculates first pixel values for which accumulated frequencies are 5% or more, 50% or more, and 95% or more of the total frequency as a minimum value, intermediate value, and maximum value. This histogram analysis is merely an example. Alternatively or additionally, various other methods may be used. For example, a method of extracting a modal value of the histogram as a representative value, i.e., an analysis value may be used.

Next, the analysis value storage unit304stores the image analysis result of the current frame (S405). The analysis value comparison unit303calculates differences between the image analysis values at the acquisition timing of the current frame and those at the acquisition timing of the previous frame, and outputs the calculated differences to the total change determination unit341(S406).FIG. 6shows an example of the image analysis values of the current frame, those of the previous frame, and their differences. As can be seen fromFIG. 6, a variation amount of the minimum values is −50, that of the intermediate values is +52, and that of the maximum values is +212. The differences of the minimum values, intermediate values, and maximum values are calculated as the histogram analysis by the image variation analysis unit301. However, the present invention is not limited to this example. For example, a method of calculating average values within certain ranges from the central point of an image, and calculating a difference between average values may be used. Also, various other methods that allow comparison between images such as a method of calculating a sum total of values representing pixel value differences between images, and a method of performing comparison of motion vectors between images may be applied.

Steps S411to S413executed by the position change determination unit311will be described below. The position storage unit312in the position change determination unit311receives the position information of the X-ray tube in the irradiation device from the irradiation device102, the bed position information from the bed103, and the X-ray sensor position information from the gantry104at the acquisition timing of the current frame image (S411). Then, the position storage unit312stores the information regarding the position of the X-ray tube in the irradiation device from the irradiation device102, the bed position information from the bed103, and the X-ray sensor position information from the gantry104at the acquisition timing of the current frame image (S412).

The position calculation unit313then performs relative position change determination between the exposure field and observation portion based on pieces of the position information of the X-ray tube, bed, and X-ray sensor at the acquisition timings of the current and previous frame images (S413). Then, the position calculation unit313outputs the result to the total change determination unit341(S413). An example of the position change determination method executed by the position change determination unit313will be described below.FIG. 7is a view showing an X-ray tube801, bed802, and gantry803, and position information in the X-, Y-, and Z-directions.FIGS. 8A and 8Bare tables showing pieces of position information, from an origin or a respective origin, of the X-ray tube, of the bed, and of the X-ray sensor at the acquisition timings of the current and previous frame images, and position differences between the frames. Note that the origins may be different for each object and may be positions at the beginning of image sensing. As shown inFIG. 8A, from the position at the acquisition timing of the previous frame image, the X-ray tube has moved by 0 cm in the X-direction, 0 cm in the Y-direction, and 0 cm in the Z-direction, and the bed has moved by +10 cm in the X-direction, −5 cm in the Y-direction, and 0 cm in the Z-direction. Also, the X-ray sensor has moved by +10 cm in the X-direction, −5 cm in the Y-direction, and 0 cm in the Z-direction. As shown inFIG. 8B, from the position at the acquisition timing of the previous frame image, the X-ray tube has moved by +10 cm in the X-direction, +10 cm in the Y-direction, and +5 cm in the Z-direction, and the bed has moved by +10 cm in the X-direction, +10 cm in the Y-direction, and +5 cm in the Z-direction. Also, the X-ray sensor has moved by +10 cm in the X-direction, +10 cm in the Y-direction, and +5 cm in the Z-direction.

An example of conditions that allow to determine that the relative positional relationship has not changed is as follows.
X(tube moving amount)=X(bed moving amount)=X(X-ray sensor moving amount)
and
Y(tube moving amount)=Y(bed moving amount)=Y(X-ray sensor moving amount)
and
Z(tube moving amount)=Z(bed moving amount)=Z(X-ray sensor moving amount)

From the above conditions, a result “relative position change detected” is determined in case ofFIG. 8A, and a result “relative position change not detected” is determined in case ofFIG. 8B.

Note that as the relative position change determination method, various other methods that determine a change in relative positional relationship among the X-ray tube, bed, and X-ray sensor may be applied in addition to the aforementioned method. For example, a method of receiving information from the irradiation device102, bed103, and gantry104only when any of the X-ray tube, bed, and X-ray sensor have moved, and calculating the moving amounts may be applied. Alternatively, a method of determining that the positions have changed when only one or two of the X-ray tube, bed, and X-ray sensor have moved may be applied. Alternatively, a method of determining that the positions have changed when respective moving amount differences are equal to or larger than a threshold (e.g., ±1 cm), and determining that the positions have not changed when the differences are less than the threshold range may be applied.

Steps S421to S423executed by the imaging condition change determination unit321will be described below. The imaging condition storage unit322in the imaging condition change determination unit321receives imaging conditions such as a tube voltage, tube current, and irradiation time from the irradiation device102and a frame rate of the X-ray sensor from the gantry104at the acquisition timing of the current frame image (S421). The imaging condition storage unit322stores information of the tube voltage, tube current, irradiation time, and frame rate at the acquisition timing of the current frame image (S422).

Next, the imaging condition comparison unit323performs imaging condition change determination from pieces of tube voltage information, pieces of tube current information, pieces of irradiation time information, and pieces of frame rate information at the acquisition timings of the current and previous frame images. Then, the imaging condition comparison unit323outputs that result to the total change determination unit341(S423). An example of the imaging condition change determination performed by the imaging condition comparison unit323will be described below.FIGS. 9A and 9Bare tables showing the pieces of tube voltage information, pieces of tube current information, pieces of irradiation time information, and pieces of frame rate information at the acquisition timings of the current and previous frame images, and differences of respective conditions.FIG. 9Ashows an example in which the tube voltage has changed by +5 kV, the tube current has changed by +50 mA, the irradiation time has changed by 0 ms, and the frame rate has changed by 0 fps from the acquisition timing of the previous frame image.FIG. 9Bshows an example in which the tube voltage has changed by +2 kV, the tube current has changed by 0 mA, the irradiation time has changed by 0 ms, and the frame rate has changed by 0 fps.

An example of conditions that allow to determine that the imaging conditions have not changed is as follows.
−3(kV)<tube voltage(change amount)<3(kV)
and
tube current(change amount)=0(mA)
and
irradiation time(change amount)=0(msec)
and
frame rate(change amount)=0(fps)

From the above conditions, the result “imaging condition change detected” is determined in case ofFIG. 9A, and “imaging condition change not detected” is determined in case ofFIG. 9B. Note that as the imaging condition change determination method, a method of receiving information from the irradiation device102and gantry104when any of the tube voltage, tube current, irradiation time, and frame rate has changed, and calculating change amounts may be applied in addition to the aforementioned method. Alternatively, a method of determining that the imaging conditions have changed when any of the tube voltage, tube current, irradiation time, and frame rate has changed may be applied.

Next, steps S431to S433executed by the biological information variation analysis unit331will be described below. In this case, as biological information analysis, analysis of a respiration phase obtained from an organ of respiration will be exemplified. The biological information storage unit332in the biological information variation analysis unit331receives respiration phase information at the acquisition timing of the current frame image (S431). The biological information storage unit332stores the respiration phase information at the acquisition timing of the current frame image (S432). Then, the biological information analysis unit333performs respiration phase variation analysis based on respiration phases at the acquisition timings of the current and previous frame images, and outputs that result to the total change determination unit341(S433). An example of the respiration phase variation analysis performed by the biological information analysis unit333will be described below.FIGS. 10A and 10Bare tables showing an example of respiration phases at the acquisition timings of the current and previous frame images, and change states. InFIG. 10A, the respiration phase at the acquisition timing of the previous frame image is an inspiratory (i.e. during inhalation) phase, while that at the acquisition timing of the current frame image is an expiratory phase. As can be seen fromFIG. 10B, the respiration phase at the acquisition timing of the previous frame image is an expiratory phase, while that at the acquisition timing of the current frame image is also an expiratory phase. Note that as the biological information change analysis method, a method of determining if each of an expiratory phase and inspiratory phase corresponds to an early stage or last stage may be applied in addition to the aforementioned method. Alternatively, various other methods of analyzing variations of biological information such as a method of receiving information from an electrocardiogram as biological information, and analyzing changes of a diastole phase and systole phase may be applied.

The total change determination method executed by the total change determination unit341will be described below. The total change determination unit341receives the image variation analysis result of the image variation analysis unit301, and the relative position change determination result of the position change determination unit311(S441). Also, the total change determination unit341receives the imaging condition change determination result of the imaging condition change determination unit321, and the biological information variation analysis result of the biological information variation analysis unit331(S441). Then, the total change determination unit341determines from the acquired analysis/determination results whether or not a large change in relative position between the observation portion and exposure field, a large change in imaging condition, and a large change in observation portion are detected, and outputs the determination result to the feature amount setting unit107(S442).

An example of the total change determination method executed by the total change determination unit341will be described below.FIGS. 11A to 11Cshow examples of the acquired analysis/determination results. InFIG. 11A, the image variation analysis result indicates [minimum value=−20, intermediate value=−30, maximum value=−40], and the relative position change determination result indicates [relative position change not detected]. Also, the irradiation condition change determination result indicates [irradiation condition change not detected], and the biological information analysis result indicates [expiratory phase→expiratory phase]. InFIG. 11B, the image variation analysis result indicates [minimum value=+100, intermediate value=+150, maximum value=+100], and the relative position change determination result indicates [relative position change detected]. Also, the irradiation condition change determination result indicates [irradiation condition change detected], and the biological information analysis result indicates [inspiratory phase→inspiratory phase]. InFIG. 11C, the image variation analysis result indicates [minimum value=+50, intermediate value=+60, maximum value=+100], and the relative position change determination result indicates [relative position change not detected]. Also, the irradiation condition change determination result indicates [irradiation condition change not detected], and the biological information analysis result indicates [inspiratory phase→expiratory phase].

An example of conditions that allow to determine that a large change in relative position between the observation portion and exposure field, a large change in imaging condition, and a large change in observation portion are detected is as follows.

(Condition 1) When the change determination result of the relative position between the observation portion and exposure field is “change detected”.

(Condition 2) When the imaging condition change determination result is “change detected”.

(Condition 3) When the variation analysis results of respective images are all ±100 or more.

(Condition 4) When the variation analysis results of respective images are all ±50 or more and the respiration phase variation analysis result is [expiratory phase→expiratory phase] or [inspiratory phase→expiratory phase].

When one or more of Conditions 1 to 4 are satisfied, it is determined that a large change in relative position between the observation portion and exposure field, a large change in imaging condition, and a large change in observation portion are detected.

From the above conditions, the result “change not detected” is determined in case ofFIG. 11A, “change detected is determined in case ofFIG. 11B, and “change detected” is determined in case ofFIG. 11C. Condition 4 is used to determine, when an image includes a relatively large change which is not an apparent variation corresponding to a large change in observation portion, whether or not that change is caused by the motion of the observation portion due to respiration. By adding Condition 4, determination errors due to the motion of the observation portion can be eliminated. Even when the analysis values do not include any large change, determination precision as to whether or not to update can be enhanced using both the imaging condition change determination result and relative position change determination result.

Note that the total change determination conditions are not limited to the aforementioned conditions. For example, a method of determining the result “change not detected” even when it is determined that changes in relative position and imaging condition are detected but when all the image analysis results fall within a range of ±50 may be applied. Alternatively, every combination that allows the obtaining of total determination results such as a method of referring to only the image analysis results, and a method of referring to only changes in relative position and imaging condition may be applied.

By executing the series of processes in steps S401to S442, the change determination by the change determination unit106is complete.

The feature amount setting processing by the feature amount setting unit107will be described in detail below with reference to the block diagram shown inFIG. 12and the flowchart shown inFIG. 13.

The feature amount setting unit107includes a process branch unit1201, previous frame feature amount extraction unit1202, feature amount storage unit1203, and current frame feature amount extraction unit1204.

The process branch unit1201receives the change determination result from the change determination unit106, and branches processes. The current frame feature amount extraction unit1204receives information indicating that the process is to be executed from the process branch unit1201, and the current frame image from the image input unit101, extracts feature amounts from the current frame image, and outputs the extracted feature amounts to the image processing unit108and feature amount storage unit1203. The feature amount storage unit1203receives and stores the feature amounts of the current frame image from the current frame feature amount extraction unit1204. The previous frame feature amount extraction unit1202receives information indicating that the process is to be executed from the process branch unit1201and feature amounts of the previous frame image from the feature amount storage unit1203, and outputs the feature amounts to the image processing unit108.

A series of processes of the feature amount setting method executed by the feature amount setting unit107will be described below with reference toFIG. 13.

At the end timing of step S1301inFIG. 13, either processes in steps S1311and S1312performed by the previous frame feature amount extraction unit1202or those in steps S1321to S1325performed by the current frame feature amount extraction unit1204are executed. That is, when steps S1311and S1312performed by the previous frame feature amount extraction unit1202are to be executed, steps S1321to S1325are skipped. When steps S1321to S1325performed by the current frame feature amount extraction unit1204are executed, steps S1311and S1312are skipped.

The process branch method performed by the process branch unit1201will be described first. The process branch unit1201branches processes based on the determination result acquired from the change determination unit106. When the determination result acquired from the change determination unit106indicates [variation not detected], the process branch unit1201instructs the previous frame feature amount extraction unit1202to operate. When the determination result indicates [variation detected], the process branch unit1201instructs the current frame feature amount extraction unit1204to operate (S1301).

(Previous Frame Feature Amount Extraction Processing)

Upon reception of an operation instruction from the process branch unit1201, the previous frame feature amount extraction unit1202acquires feature amounts of the previous frame from the feature amount storage unit1203(S1311). The previous frame feature amount extraction unit1202outputs the acquired feature amounts to the image processing unit, thus ending the processing (S1312).

(Current Frame Feature Amount Extraction Processing)

Upon reception of an operation instruction from the process branch unit1201, the current frame feature amount extraction unit1204acquires the current frame image from the image input unit101(S1321). Next, the current frame feature amount extraction unit1204decides an analysis range for the acquired image (S1322). This is to improve the analysis precision and to reduce a time required for analysis by narrowing down the image analysis range.

An example of the analysis range decision method will be described below.FIG. 14shows the current frame image, and a range bounded by a rectangle1401corresponds to an exposure field irradiated with X-rays. When a central point in the exposure field of the current frame image is calculated, a central point1402can be calculated. A range of 70% of the area of the exposure field from the central point1402is calculated, and it can be defined as an analysis range1403. Note that various other methods of deciding the analysis range such as a method of calculating a barycenter of pixel values, and defining a range having a length of 15 cm and a width of 15 cm to have the barycenter as the center, and a method of entirely defining the interior of the exposure field as an analysis range may be applied in addition to the aforementioned analysis range decision method.

The current frame feature amount extraction unit1204calculates feature amounts within the decided analysis range (S1323). An example of the feature amount calculation method will be described below.FIG. 15Ashows a histogram generated using pixel values within only the analysis range1403inFIG. 14.FIG. 15Bshows a trim histogram generated by setting the values of frequencies equal to or smaller than a certain threshold (e.g.,10) to be zero inFIG. 15A. A minimum value, maximum value, and intermediate value of the trim histogram shown inFIG. 15Bare respectively calculated as feature amounts.FIG. 15Cshows an example in which the minimum value, maximum value, and intermediate value of the trim histogram shown inFIG. 15Bare respectively calculated as feature amounts. Note that various other methods of extracting feature amounts of the current frame image such as a method of calculating variance and average values within the analysis range and a method of calculating a difference between the minimum and maximum values may be applied in addition to the aforementioned feature amount calculation method.

Next, the current frame feature amount extraction unit1204outputs the calculated feature amounts to the image processing unit108(S1324). The current frame feature amount extraction unit1204then outputs the calculated analysis values (feature amounts) to the feature amount storage unit1203, which stores the received feature amounts (S1325), thus ending the processing.

Note that as the current frame feature amount extraction method, various other methods of extracting feature amounts such as a method of defining the analysis values of the current frame calculated by the image variation analysis unit301in the change determination unit106as feature amounts may be applied in addition to the aforementioned method.

By executing the processes in steps S1301to S1325as needed, the feature amount setting processing by the feature amount setting unit107is complete.

The image processing method by the image processing unit108will be described in detail below with reference to the block diagram shown inFIG. 16and the flowchart shown inFIG. 17.

The image processing unit108includes a tone processor1601, sharpening processor1602, and noise suppression processor1603. The tone processor1601receives the current frame image from the image input unit101and the feature amounts from the feature amount setting unit107, and performs tone processing. The sharpening processor1602receives the image after the tone processing from the tone processor1601and the feature amounts from the feature amount setting unit107, and performs sharpening processing so as to sharpen the edge of a subject image. The noise suppression processor1603receives the image after the sharpening processing from the sharpening processor1602and the feature amounts from the feature amount setting unit107, and performs noise suppression processing.

FIG. 17is a flowchart showing a series of processes of the image processing method performed by the image processing unit108. The tone processor1601performs the tone processing based on the current frame image acquired from the image input unit101and the feature amounts acquired from the feature amount setting unit107(S1701). An example of the tone processing method by the tone processor1601will be described below.

The tone processor1601acquires the feature amounts (minimum value, intermediate value, and maximum value) from the feature amount setting unit107. The tone processor1601generates a lookup table (to be abbreviated as an LUT hereinafter) required to convert pixel values of the current frame image into those after the tone conversion processing, based on the feature amounts, and a standard pixel value and fixed value conversion value, which are set in advance.

FIG. 18shows an example of the LUT generated based on the feature amounts shown inFIG. 15C. In this LUT, points which respectively convert a pixel value “0” of the current frame image into “512” and “4095” into “4095”, and also convert the minimum value as the feature amount into “700”, the intermediate value into “2000”, and the maximum value into “3700”, and table values between neighboring points are calculated by spline interpolation. The tone processor1601converts respective pixel values of the current frame image with reference to the LUT, thereby generating a tone-processed image.

In addition to the aforementioned tone processing method, for example, the following method may be applied. That is, in this method, an image is decomposed based on spatial frequencies to generate a plurality of images having various spatial frequency bands. Then, conversion coefficients or LUTs are generated based on feature amounts of the respective images to apply conversion processing to the respective images. Then, the processed images are reconstructed so as to attain tone conversion.

The sharpening processing method performed by the sharpening processor1602will be described below. The sharpening processor1602performs the sharpening processing based on the image after the tone processing (tone-processed image) acquired from the tone processor1601and the feature amount acquired from the feature amount setting unit107(S1702). An example of the sharpening processing by the sharpening processor1602will be described below. The sharpening processor1602decides emphasis coefficients according to the feature amounts (minimum value, maximum value) acquired from the feature amount setting unit107. At this time, the emphasis coefficients may be increased with decreasing difference between the minimum value and maximum value. This is because when the difference between the minimum value and maximum value is small, since a dynamic range is narrow, it is difficult even for a high-spatial frequency region to give high contrast. Next, the sharpening processor1602applies average value filter processing of 3 pixels×3 pixels to the tone-processed image to generate a blur image. Then, the sharpening processor1602performs processing for subtracting the blur image from the tone-processed image (difference processing). After a difference image is generated, the sharpening processor1602multiplies this difference image by coefficients, and adds the processed image to the tone-processed image, thereby generating a sharpening-processed image. Note that in addition to the aforementioned sharpening processing method, the following method may be applied. That is, in this method, an image is decomposed based on spatial frequencies to generate a plurality of images having various spatial frequency bands. Then, conversion coefficients or LUTs are generated based on feature amounts of the respective images to apply conversion processing to the respective images. Then, the processed images are reconstructed so as to attain the sharpening processing.

The noise suppression processing method by the noise suppression processor1603will be described below. The noise suppression processor1603performs noise suppression processing based on the image after the sharpening processing (sharpening-processed image) acquired from the sharpening processor1602, and the feature amounts acquired from the feature amount setting unit107(S1703). An example of the noise suppression processing by the noise suppression processor1603will be described below. The noise suppression processor1603decides a smoothing filter size according to the feature amount (minimum value) acquired from the feature amount setting unit107. At this time, smoothing coefficients may be increased with decreasing minimum value. This is because when the minimum value is small, since the dose is small, an image includes a relatively large number of noise components. Next, the noise suppression processor1603applies smoothing filter processing to the sharpening-processed image using the decided filter size to generate a noise-suppression processed image. Note that in addition to the aforementioned noise suppression processing method, the following method may be applied. That is, in this method, an image is decomposed based on spatial frequencies to generate a plurality of images having various spatial frequency bands. Then, conversion coefficients or LUTs are generated based on feature amounts of the respective images to apply conversion processing to the respective images. Then, the processed images are reconstructed so as to attain the noise suppression processing.

Note that the method of sequentially applying the tone processing, sharpening processing, and noise suppression processing has been exemplified as the image processing method. In addition, a method of operating these processes in parallel may be applied. Alternatively, a method using the feature amounts in only one process (e.g., tone processing) and using fixed values in other processes may be applied. Alternatively, the processing order of the above three processes may be changed, or one or a plurality of the three processes may be combined.

By executing the series of processes in steps S1701to S1703, the image processing by the image processing unit108is complete.

As described above, according to this embodiment, any influences of feature amount variations can be suppressed, a quick response to image changes can be assured, the load on the arithmetic processing can be reduced, and stable image quality can be realized.

Second Embodiment

A schematic arrangement of an image processing apparatus according to the second embodiment will be described below with reference to the block diagram shown inFIG. 19and the flowchart shown inFIG. 20. In the arrangement shown inFIG. 19, the same reference numerals denote the same components as inFIG. 1, and a description thereof will not be repeated.

A tentative feature amount extraction unit1907shown inFIG. 19receives an image from an image input unit101, and extracts tentative feature amounts. A tentative feature amount is defined as the feature amount obtained by executing, in parallel, a step of extracting feature amounts (tentative feature amounts) of the current frame image (S2002) and a step of determining whether or not any of the changes is detected (S2003). In other words, tentative feature amounts are those that, in the present embodiment, are used for the determination of a detected change between frames, and that are extracted. In the first embodiment, the step S203of extracting the current feature amount is executed in serial after the determining step (S202—change detected) has been completed. In order to distinguish the feature amounts extracted in S203of the first embodiment shown inFIG. 2from the feature amounts extracted in S2002of the second embodiment shown inFIG. 20, the extracted feature amounts of the second embodiment are labeled “tentative feature amounts”. A feature amount setting unit1908receives a change determination result from a change determination unit106and the tentative feature amounts from the tentative feature amount extraction unit1907, and sets feature amounts of the image. An image processing unit108performs image processing based on the feature amounts set by the feature amount setting unit1908.

The processing sequence of the image processing apparatus according to the second embodiment will be described below with reference toFIG. 20. Note that steps S2002and S2003inFIG. 20operate in parallel (rather than in series as in the first embodiment), and at the end times of these processes, processes from step S2004are executed.

The image input unit101accepts an input of one frame (to be referred to as a current frame image hereinafter) in a moving image (S2001).

Next, the tentative feature amount extraction unit1907receives the current frame image from the image input unit101, and extracts feature amounts (tentative feature amounts) of the current frame image (S2002). Note that the tentative feature amount extraction method by the tentative feature amount extraction unit1907can use, for example, the same method as the current frame feature amount extraction method by a current frame feature amount extraction unit1204inFIG. 12.

The change determination unit106receives the input image from the image input unit101, imaging condition change information and position change information of an X-ray tube input from an irradiation device102, and bed position change information input from a bed103. Also, the change determination unit106receives sensor position change information input from a gantry104and biological information change information input from a biological information monitor105. Then, the change determination unit106determines whether or not a large change in relative position between an observation portion and exposure field, a large change in imaging condition, and a large change in observation portion are detected (S2003). Note that the change determination method by the change determination unit106can use the same method as that described in the first embodiment.

If the change determination unit106determines that changes are detected, or if it is determined that the current frame is the first frame at the beginning of imaging, the feature amount setting unit1908sets the tentative feature amounts extracted by the tentative feature amount extraction unit as feature amounts of the current frame image.

If the change determination unit106determines that no changes are detected, and if it is determined that the current frame is not the first frame at the beginning of imaging, the feature amount setting unit1908sets feature amounts set for a previous frame image as those of the current frame image (S2004).

Then, the image processing unit108performs image processing such as tone conversion processing, sharpening processing, and noise suppression processing based on the feature amounts set by the feature amount setting unit1908(S2005), and outputs the processed image. A series of processes in the current frame of the moving image are complete. Note that the image processing method by the image processing unit108can use the same method as that described in the first embodiment.

As described above, according to this embodiment, any influences of feature amount variations can be suppressed, a quick response to image changes can be assured, the load on the arithmetic processing can be reduced, and stable image quality can be realized.

Other Embodiments

This application claims the benefit of Japanese Patent Application No. 2009-090484, filed Apr. 2, 2009, which is hereby incorporated by reference herein in its entirety.