Abstract:
A spectral image processing method is capable of reducing noise while maintaining necessary information. The spectral image processing method performs processing on a spectral image of a specimen, including a step of normalizing spectra (=spectral brightness curves) of respective pixels constituting the spectral image such that their brightness levels become equal, a step of smoothing the normalized spectra in spatial directions of the respective pixels, and a step of denormalization of multiplying spectra of the respective pixels obtained by the smoothing by either one of brightness levels of the pixels corresponding the spectra and values corresponding to the brightness levels. Consequently, the noise can be reduced while information on brightness distribution on the image is maintained.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a 371 of International Application Number PCT/JP2007/051698, filed Feb. 1, 2007, which claims the priority of Japanese Patent Application Number 2006-046508 filed Feb. 23, 2006. 
     TECHNICAL HELD 
     The present invention relates to a spectral image processing method of processing a spectral image acquired by a microscope or the like and a computer-executable spectral image processing program. Further, the present invention relates to a spectral imaging system such as a spectral-imaging fluorescent laser microscope. 
     BACKGROUND ART 
     In dynamic observation of an organism cell, a sample is labeled by a fluorescent material such as a fluorescent reagent or a fluorescent protein and observed by an optical microscope such as a fluorescent laser microscope in some cases. When plural fluorescent materials are used simultaneously, it is necessary to detect images of respective wavelength components (a spectral image). 
     However, when emission wavelengths of the plural fluorescent materials overlap, the images of these respective materials cannot be separated by the optical microscope, so that an analysis method of importing the spectral image detected by the microscope into a computer and separating (unmixing) it into the images of the respective materials becomes effective (see Non-Patent Document 1 or the like). Incidentally, in this unmixing, emission spectral data of the respective materials disclosed by manufacturers of reagents and the like is used.
     Non-Patent Document 1: Timo Zimmermann, JensRietdorf, Rainer Pepperkok, “Spectral imaging and its applications in live cell microscopy”, FEBS Letters 546 (2003), P 87-P 92, 16 May 2003   

     DISCLOSURE 
     Problems to be Solved 
     However, measurement noise is superimposed on a spectral image being actual measurement data due to instability of a light source of a microscope, electric noise of a light detecting element of the microscope, and so on, which exerts a strong influence on the accuracy of unmixing. In particular, when spectra of plural fluorescent reagents are similar, for example, when peak wavelengths are close to each other, the accuracy of unmixing becomes worse if the measurement noise is large. 
     Among measures against this is a method of smoothing adjacent images by performing spatial filter processing, for example, averaging filter processing or median-filter processing, which is effective as a method of reducing noise. However, in such a method, brightnesses are also averaged, which causes a problem that spatial resolution is deteriorated and on a simple average, the influence of a pixel with a high brightness increases, so that the noise reduction is not necessarily sufficient. 
     Hence, an object of the present invention is to provide a spectral image processing method capable of reducing noise without damaging necessary information as much as possible and a computer-executable spectral image processing program. Further, an object of the present invention is to provide a high-performance spectral imaging system. 
     Means for Solving the Problems 
     A spectral image processing method of the present invention is a spectral image processing method of performing processing on a spectral image of a specimen, including: a step of normalizing spectra (=spectral brightness curves) of respective pixels constituting the spectral image such that their brightness levels become equal; a step of smoothing the normalized spectra in spatial directions of the respective pixels; and a step of denormalization of multiplying spectra of the respective pixels obtained by the smoothing by either one of brightness levels of the pixels corresponding the spectra and values corresponding to the brightness levels. 
     Incidentally, the normalization is performed such that brightness integral values of the spectra become equal, and the denormalization is performed such that the brightness integral values of the spectra return to values before the normalization. 
     Moreover, the normalization is performed such that brightness maximum values of the spectra become equal, and the denormalization is performed such that the brightness maximum values of the spectra return to values before the normalization. 
     Further, another spectral image processing method of the present invention includes an unmixing step of, based on a spectral image subjected to image processing using any spectral image processing method of the present invention and emission spectral information of plural materials contained in the specimen, separating and finding respective contributions of the plural materials to the spectral image. 
     Furthermore, a spectral image processing program of the present invention causes a computer to execute any spectral image processing method of the present invention. 
     Moreover, a spectral imaging system of the present invention includes: a spectral imaging unit which acquires a spectral image of a specimen; and a spectral image processing unit which imports the acquired spectral image and executes any spectral image processing method of the present invention. 
     Effect 
     According to the present invention, a spectral image processing method capable of reducing noise without damaging necessary information as much as possible and a computer-executable spectral image processing program are realized. Further, according to the present invention, a high-performance spectral imaging system is realized. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a configuration diagram of a system of an embodiment; 
         FIG. 2  is an operational flowchart of a CPU  23 ; 
         FIG. 3  is a diagram explaining normalizing processing; 
         FIG. 4  is a diagram explaining smoothing processing and denormalizing processing; 
         FIG. 5  is a diagram showing examples of emission spectral curves S 1 , S 2 , S 3  of fluorescent reagents; 
         FIG. 6  is a diagram showing changes of spectral curves when the standard of normalization is set to a brightness maximum value; and 
         FIG. 7  is a diagram showing changes of the spectral curves when the standard of denormalization is set to the brightness maximum value. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     An embodiment of the present invention will be described. This embodiment is an embodiment of a spectral imaging fluorescent confocal laser microscope system. 
     First, the configuration of this system will be described. 
       FIG. 1  is a configuration diagram of this system. As shown in  FIG. 1 , this system includes a main body of a microscope  10 , a computer  20  connected thereto, and an input device  30  and a displaying device  40  connected thereto. The input device  30  is a mouse, a keyboard, and so on, and the displaying device  40  is an LCD or the like. 
     In the main body  10 , a laser light source  11 , a dichroic mirror  12 , an optical scanner  13 , an objective lens  14 , a sample  15 , an observation lens  16 , a pinhole mask  17 , a spectroscopic element  18 , and a multichannel light detector  19  are placed. The sample  15  is labeled by plural types (for example, three types) of fluorescent reagents, and the multichannel light detector  19  has many (for example, 32) wavelength channels. 
     The computer  20  includes a CPU  23 , a ROM  24  into which a basic operation program of the CPU  23  is written, a RAM  25  used as a temporary storage means while the CPU  23  is operating, a hard disk drive  26  to save information for a long time. an interface circuit  27  interfacing the input device  30  and the displaying device  40 , A/D converting circuits  21   1 ,  21   2 , . . . ,  21   32  of the same number as wavelength channels of the multichannel light detector  19 , and frame memories  22   1    22   2  . . . ,  22   32  of the same number as the A/D converting circuits. The frame memories  22   1 ,  22   2 , . . . ,  22   32 , the hard disk drive  26 , the CPU  23 , the ROM  24 , the RAM  25 , the interface circuit  27  are connected via a bus  20 B. An operation program of the CPU  23  necessary for this system is previously stored in the hard disk drive  26 . 
     Laser light (for example, having a wavelength of 488 nm) is emitted from the laser light source  11  of the main body of the microscope  10 . This laser light is reflected by the dichroic mirror  12  and collected at a point on the sample  15  via the optical scanner  13  and the objective lens  14  in order. At the light collecting point, fluorescence (for example, having a wavelength of 510 nm to 550 nm) is generated, and when entering the dichroic mirror  12  via the objective lens  14  and the optical scanner  13  in order, the fluorescence is transmitted through this dichroic mirror  12  and enters the pinhole mask  17  via the observation lens  16 . This pinhole mask  17  forms a conjugate relation with the sample  15  by the observation lens  16  and the objective lens  14  and has a function of letting only a necessary ray of light of the fluorescence generated on the sample  15  pass therethrough. As a result, a confocal effect of the main body of the microscope  10  can be obtained. When entering the spectroscopic element  8 , the fluorescence which has passed through the pinhole mask  17  is separated into plural wavelength components. These respective wavelength components enter wavelength channels different from each other of the multichannel light detector  19  and detected independently and simultaneously. 
     The respective wavelength channels (here, 32 wavelength channels) of the multichannel light detector  19  detect, for example, 32 kinds of wavelength components different in steps of 5 nm in a wavelength range from 510 nm to 550 nm. Respective signals outputted from the 32 wavelength channels are imported in parallel into the computer  20  and individually inputted to the frame memories  22   1 ,  22   2 , . . . ,  22   32  via the A/D converting circuits  21   1 ,  21   2 , . . . ,  21   32 . 
     This multichannel light detector  19  and the optical scanner  13  are synchronously driven, and thereby the signals are repeatedly outputted from the multichannel light detector  19  during a period of two-dimensional scanning at the light collecting point on the sample  15 . At this time, images of the respective wavelength channels of the sample  15  are gradually accumulated in the frame memories  22   1 ,  22   2 , . . . ,  22   32 . The images (channels images D 1 , D 2 , . . . , D 32d ) of the respective wavelength channels accumulated in the frame memories  22   1 ,  22   2 , . . . ,  22   32  are read in an appropriate timing by the CPU  23 , integrated into one spectral image F, and then stored in the hard disk drive  26 . 
     Incidentally, in the hard disk drive  26  of the computer  20 , in addition to this spectral image F, emission spectral data of the fluorescent reagents used for the sample  15  is previously stored. This emission spectral data is disclosed by manufactures of the fluorescent reagents or the like and loaded into the computer  20 , for example, by the Internet, a storage medium, or the like. 
     Next, the operation of the CPU  23  after the spectral image F is acquired will be described. 
       FIG. 2  is an operational flowchart of the CPU  23 . As shown in  FIG. 2 , after executing noise reducing processing constituted by normalizing processing (step S 1 ), smoothing processing (step S 2 ), and denormalizing processing (step S 3 ), the CPU  23  executes unmixing processing (step S 4 ), and displaying processing (step S 5 ). These steps will be described below step by step. 
     Normalizing Processing (step S 1 ): 
     In this step, first, as shown in  FIG. 3(A) , the CPU  23  refers to spectral curves of respective pixels from the spectral image F. In  FIG. 3(A) , only spectral curves of some four pixels (a first pixel, second pixel, third pixel, fourth pixel) are shown. The horizontal axis of the spectral curve is a wavelength channel, and the vertical axis thereof is a brightness value. 
     Brightness levels of the spectral curves of the respective pixels vary as shown in  FIG. 3(A) . A brightness integral value A 1  of the spectral curve of the first pixel indicates a total brightness of the first pixel, a brightness integral value A 2  of the spectral curve of the second pixel indicates a total brightness of the second pixel, a brightness integral value A 3  of the spectral curve of the third pixel indicates a total brightness of the third pixel, and a brightness integral value A 4  of the spectral curve of the fourth pixel indicates a total brightness of the fourth pixel. 
     Further, as shown in  FIG. 3(A) , shapes of the spectral curves vary among the respective pixels. Between close pixels, there is a high possibility that rough shapes of the spectral curves are similar, but fine shapes of the spectral curves differ from each other even if the pixels are close since random measurement noise is superimposed. 
     Then, as shown in  FIG. 3(B) , the CPU  23  normalizes the spectral curves of the respective pixels such that their brightness integral values A become one. In the normalization of each spectral curve, it is only required to multiply brightness values of the respective wavelength channels of the spectral curve by a normalizing coefficient=(1/current brightness integral value). 
     When a spectral image F′ constituted by the normalized spectral curves is referred to here as shown at the right side of  FIG. 3 , any of the total brightnesses of the respective pixels becomes one in the spectral image F′. That is to say, brightness information of the spectral curves of the respective pixels is excluded from the spectral image F′, and only shape information of the spectral curves of the respective pixels is maintained. Hereinafter, respective wavelength components (channel images) of this spectral image F′ are represented as D 1 ′, D 2 ′, . . . , D 32 ′. 
     Smoothing Processing (step S 2 ): 
     In this step, as shown in  FIG. 4(A) , the CPU  23  performs averaging filter processing on each of the above channel images D 1 ′, D 2 ′, . . . , D 32 ′. Therefore, each of the channel images D 1 ′, D 2 ′, . . . , D 32 ′ is smoothed in a spatial direction. 
     In the averaging filter processing for the channel image D′, a mask (which is a computational mask), for example, having an opening of three pixels high by three pixels wide is used. This mask is put into the channel image D′, and the brightness value of a target pixel located at the center of the opening of the mask is replaced with a brightness mean value of all the pixels in the opening. By repeatedly performing this processing while shifting a mask position on the channel image D′, processing of the whole area of the image is completed. 
     Here, if the respective channel images after the smoothing are represented as D 1 ″, D 2 ″, . . . , D 32 ″ as shown in the lower left of  FIG. 4  and a spectral image F″ constituted by these channel images D 1 ″, D 2 ″, . . . , D 32 ″ is referred to, in the spectral image F″, as shown in  FIG. 4(B) , shapes of the spectral curves of the respective pixels become smooth. This is because the shapes of the spectral curves of the respective pixels are influenced by the shapes of the spectral curves of their adjacent pixels by the smoothing. This indicates that noise is removed from the shape information of the spectral curves of the respective pixels. 
     Denormalizing Processing (step S 3 ): 
     In this step, as shown in  FIG. 4(C) , the CPU  23  denormalizes the spectral curves of the respective pixels constituting the spectral image F″ such that their brightness integral values return to the brightness integral values before the normalization (see  FIG. 3(A) ). Concerning the spectral curve of the first pixel, it is denormalized such that its brightness integral value returns to the value A 1  before the normalization, concerning the spectral curve of the second pixel, it is denormalized such that its brightness integral value returns to the value A 2  before the normalization, concerning the spectral curve of the third pixel, it is denormalized such that its brightness integral value returns to the value A 3  before the normalization, and concerning the spectral curve of the fourth pixel, it is denormalized such that its brightness integral value returns to the value A 4  before the normalization. In the denormalization of each spectral curve, it is only required to multiply brightness values of the respective wavelength channels of the spectral curve by an denormalizing coefficient=(brightness integral value before normalization/current brightness integral value). 
     A spectral image constituted by the above spectral curves after the denormalization is stored again as the spectral image F in the hard disk drive  26  as shown in the lower right of  FIG. 4 . 
     In this spectral image F, the brightness information of the spectral curves of the respective pixels is recovered by the denormalization. Besides, noise is removed from the shape information of the spectral curves of the respective pixels as described above. Accordingly, this spectral image F accurately represents the state of the sample  15 . 
     Unmixing Processing (step S 4 ): 
     In this step, first, the CPU  23  reads the spectral image F and the emission spectral data of the fluorescent reagents from the hard disk drive  26 . 
     As shown in  FIGS. 5(A) , (B), (C), the emission spectral data represents emission spectral curves S 1 , S 2 , S 3  of the three types of fluorescent reagents (a first reagent, second reagent, third reagent). These emission spectral curves S 1 , S 2 , S 3  are each represented by a one-dimensional matrix such as shown in equation (1). 
     
       
         
           
             
               
                 
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     Note that an element S ij  in equation (1) is a brightness value of an ith wavelength of a jth reagent. The number of elements in a wavelength direction of this matrix is set to 32 to match the data amount in a wavelength direction of the spectral image F (=the number of wavelength channels of the multichannel light detector  19 ). 
     The CPU  23  performs unmixing processing of the spectral image F based on these emission spectral curves S 1 , S 2 , S 3 , and the unmixing is performed for each pixel of the spectral image F. 
     A spectral curve f of some pixel included in the spectral image F is represented by a one-dimensional matrix such as shown in equation (2). An element f l  is a brightness value of an ith wavelength channel of this pixel. 
     [Equation 2] 
     
       
         
           
             
               
                 
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     Accordingly, if the contribution ratio of the first reagent to this pixel is taken as p 1 , the contribution ratio of the second reagent thereto is taken as p 2 , and the contribution ratio of the third reagent thereto is taken as p 3 , the spectral curve f of this pixel is represented by equation (3).
 
[Equation 3]
 
 f=S   1   ·p   1   +S   2   ·p   2   +S   3   ·p   3   (3)
 
     Further, if the respective emission spectral curves of the three types of fluorescent reagents are brought together and represented by one matrix S as shown in equation (4), and the respective contribution ratios of the three types of fluorescent reagents are brought together and represented by one matrix P as shown in equation (5), equation (3) is transformed as shown in equation (6).
 
[Equation 4]
 
 S=[S   1    S   2    S   3 ]  (4)
 
                   [     Equation   ⁢           ⁢   5     ]                           P   =     [           p   1               p   2               p   3           ]             (   5   )               [Equation 6]   f=S·P   (6)
 
     Hence, the CPU  23  can unmix this pixel by assigning information on the spectral curve f of this pixel and information on the emission spectral curve S to equation (6) and solving this equation for the contribution ratio P. 
     Note, however, that since the number of wavelength channels (here, 32) is set larger than the number of types of fluorescent reagents (here, 3) as described above in this system, the CPU  23  applies a least squares method. 
     The least squares method is to prepare equation (7) with consideration given to an error a in equation (6) and find the contribution ratio P such that a square value of the error a becomes minimum.
 
[Equation 7]
 
 f=S·P+ε   (7)
 
     An equation to calculate the contribution ratio P by this least squares method is shown as in equation (8).
 
[Equation 8]
 
 P =( S   T   S ) −1   S   T   f   (8)
 
     Note that S T  is a transposed matrix of S. 
     Accordingly, the CPU unmixes this pixel by assigning the information on the spectral curve f of this pixel and the information on the emission spectral curve S to this equation (8). Then, the CPU  23  performs this unmixing on all the pixels of the spectral image F, respectively, and completes this step. 
     As just described, the unmixing processing in this step is performed by the well-known least squares method, but since the spectral image F accurately represents the state of the sample  15  as described above, the accuracy of this unmixing processing is higher than that of the conventional one. 
     Displaying Processing (step S 5 ): 
     In this step, the CPU  23  displays the information on the contribution ratios (contribution ratios of the respective fluorescent reagents to the respective pixels) found by the unmixing processing on the displaying device  40 . The information on the contribution ratios may be displayed as numeric data, but in order to intuitively inform a user of it, it is desirable that the CPU  23  creates an unmixed image colored according to the contribution ratios and displays it. 
     As described above, the computer  20  of this system removes noise from the spectral image prior to the unmixing processing, but this noise reducing processing does no damage to the brightness information of the spectral curves of the respective pixels as described above, so that the spectral image F which accurately represents the state of the sample  15  can be obtained. Hence, the accuracy of the unmixing processing by the computer  20 , that is, the performance of this system is certainly improved. 
     Incidentally, in the noise reducing processing (steps S 1  to S 3 ) of this system, the standards of the normalization and the denormalization of the spectral curve are set to the brightness integral value of the spectral curve, but may be set to a brightness maximum value or a brightness intermediate value instead of the brightness integral value. 
     In  FIG. 6  and  FIG. 7 , changes of the spectral curves when the standards of the normalization and the denormalization are set to the brightness maximum value are shown. Referring to  FIG. 6  and  FIG. 7 , it can be seen that peaks of the spectral curves of the respective pixels before the normalization are I 1 , I 2 , I 3 , I 4 , but all become one after the normalization, and after the denormalization, return to the values I 1 , I 2 , I 3 , I 4  before the normalization. 
     Further, in the smoothing processing (step S 2 ) of this system, the averaging filter processing is applied, but instead of the averaging filter processing, a different spatial filter processing such as weighted averaging filter processing or a median-filter processing may be applied. For reference&#39;s sake, the median-filter processing is to find a brightness intermediate value of all the pixels in the opening instead of calculating the brightness mean value thereof. It is desirable that the type of such filter processing be selected appropriately according to the type of the measurement noise generated in the main body of the microscope  10 . For reference&#39;s sake, the averaging filter processing is effective when nose is generated uniformly on the channel image, and the median-filter processing is effective when noise is generated suddenly on the channel image (salt-and-pepper noise). 
     Furthermore, in the smoothing processing (step S 2 ) of this system, the size of the mask (size of a filter) is 3 pixels×3 pixels=9 pixels, but may be changed to a different size. It is desirable that this size be selected appropriately according to the type of the measurement noise generated in the main body of the microscope  10 . 
     Moreover, in the noise reducing processing (steps S 1  to S 3 ) of this system, the start timing of the smoothing processing is after the normalization of the spectral curves of all the pixels, but it is also possible to normalize spectral curves of required pixels on a case-by-case basis while performing the smoothing processing. 
     Further, in this system, the operation program of the CPU  23  is previously stored in the hard disk drive  26 , but part or all of the program may be installed into the computer  20  from outside via the Internet, a storage medium, or the like. 
     Furthermore, in this system, each processing is executed by the computer  20 , but part or all of the operations of the computer  20  may be executed by a device (control/image processing device) dedicated to the main body of the microscope  10 . 
     Moreover, the main body of the microscope  10  of this system uses the multichannel light detector  19  to detect respective wavelength components of incident light, but instead of the multichannel light detector  19 , a combination of one-channel light detector and a movable mask, a combination of plural one-channel light detectors and plural filters, or the like may be used. Note, however, that the use of the multichannel light detector  19  is advantageous in that space can be saved. 
     Further, the main body of the microscope  10  of this system is a fluorescent microscope which detects fluorescence generated on the sample  15 , but may be a microscope which detects transmitted light or reflected light of light illuminating the sample  15 . In this case, instead of the dichroic mirror  12 , a beam splitter is used. 
     Furthermore, the main body of the microscope  10  of this system is a confocal microscope which confocally detects light from the sample  15 , but the function of this confocal detection may be omitted. In this case, the pinhole mask  17  becomes unnecessary. 
     Additionally, the main body of the microscope  10  of this system is a scanning microscope which optically scans the sample  15 , but may be a non-scanning microscope. In this case, the optical scanner  13  becomes unnecessary. 
     Namely, the present invention can be applied to various devices which perform spectral imaging. 
     The many features and advantages of the invention are apparent from the foregoing description. It is to be understood that the invention is not limited to the described embodiments, which are intended to be illustrative and not limiting. As will readily occur to those skilled in the art, numerous changes and modifications are possible in keeping with the principles and spirit of the invention, the scope of which is defined in the appended claims.