JP2011092438A - Image processor and medical image diagnostic apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processor and a medical image diagnostic apparatus simplifying operation for adjusting display conditions. <P>SOLUTION: An MRI apparatus 100 classifies pixels of a first image for each density value divided at predetermined intervals. The MRI apparatus 100 classifies pixels of a second image different in density distribution characteristics from the first image so that the pixels existing in the same spatial position as the pixels of the first image classified in the same group are classified in the same group, and classifies the pixel group of the second image classified in the same group, for every density value divided at the predetermined intervals. The MRI apparatus 100 then outputs a classified result. For example, the MRI apparatus 100 outputs the classified result by superimposing a bar graph showing the classified result on the pixels of the second image, on a density histogram showing the classified result on the pixels of the first image. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an image processing apparatus and a medical image diagnostic apparatus.

  In general, when displaying image data on a display unit such as a display, an image processing apparatus converts density values of pixels into luminance, and displays an image with converted luminance. For example, the image processing apparatus assigns the maximum density value to the maximum brightness, assigns the minimum density value to the minimum brightness, appropriately interpolates the density value therebetween and assigns it to a predetermined brightness, and converts the density value to the brightness.

  However, for example, when there is a region to be displayed in detail in the image, the image processing apparatus changes the assignment of luminance by accepting the adjustment of the display condition, and for example, contrast of the region to be displayed in detail. Image processing such as raising Here, the display conditions are the window width and the window level. The window width is a range of density values assigned to the range from the maximum brightness to the minimum brightness, and the window level is a density value located in the middle of the window width. It is.

  Incidentally, the image processing apparatus may perform image processing of a plurality of image data having different density distribution characteristics. For example, an image processing apparatus that handles a medical image may perform image processing of a morphological image and a functional image that have the same anatomical position. However, since the density distribution characteristics differ between the morphological image and the functional image, Adjustment of display conditions is accepted for each image data. For example, when a morphological image and a functional image are combined and displayed, the image processing apparatus displays a screen for adjusting the display condition of the morphological image and a screen for adjusting the display condition of the functional image. The display condition adjustment instruction is received.

Japanese Patent No. 4138783 JP 2006-188060 A

  However, the above-described conventional technique has a problem that the operation for adjusting the display condition becomes complicated. For example, when a morphological image and a functional image are combined and displayed, the operator must search for an optimal display condition for both the morphological image and the functional image having different density distribution characteristics while switching a plurality of screens. The operation was complicated. This problem is not limited to image processing apparatuses that handle medical images, and is a problem common to image processing apparatuses that perform image processing of a plurality of image data having different density distribution characteristics.

  The present invention has been made in view of the above, and an object of the present invention is to provide an image processing apparatus and a medical image diagnostic apparatus capable of simplifying an operation for adjusting display conditions.

  In order to solve the above-described problem and achieve the object, the image processing apparatus according to claim 1, first classification means for classifying pixels of the first image for each density value divided at a predetermined interval; Pixels in the second image having density distribution characteristics different from those in the first image are classified into pixels in the same spatial position as the pixels in the first image classified in the same classification by the first classification means. And the second classification means for classifying the pixel groups of the second image classified into the same classification for each density value classified at a predetermined interval, and classified by the second classification means Output means for outputting a classification result.

  In order to solve the above-described problems and achieve the object, the medical image diagnostic apparatus according to claim 5 includes: a first image imaging unit that captures a first image; and a density distribution characteristic of the first image. Second image capturing means for capturing different second images, first classification means for classifying the pixels of the first image for each density value divided at a predetermined interval, and pixels of the second image, The pixels in the same spatial position as the pixels of the first image classified into the same classification by one classification means are classified so as to be classified into the same classification, and the second image classified into the same classification is classified. The image processing apparatus includes a second classification unit that classifies the pixel group for each density value divided at a predetermined interval, and an output unit that outputs a classification result classified by the second classification unit.

  According to the present invention described in claim 1 or 5, there is an effect that an operation for adjusting the display condition can be simplified.

FIG. 1 is a block diagram illustrating the configuration of the MRI apparatus according to the first embodiment. FIG. 2 is a functional block diagram illustrating detailed configurations of the storage unit and the control unit. FIG. 3 is a diagram for explaining the volume data stored in the volume data storage unit. FIG. 4 is a diagram for explaining the density histogram. FIG. 5 is a diagram showing a data table of density histograms and mapping data. FIG. 6 is a diagram for explaining the combined histogram. FIG. 7 is a diagram for explaining adjustment of the window width and the window level. FIG. 8 is a flowchart illustrating a processing procedure performed by the MRI apparatus according to the first embodiment. FIG. 9 is a block diagram illustrating the configuration of the image processing apparatus according to the second embodiment. FIG. 10 is a diagram for explaining the use of the combined histogram. FIG. 11 is a diagram for explaining the use of the combined histogram.

  Embodiments of an image processing apparatus and a medical image diagnostic apparatus according to the present invention will be described below. In Example 1, a magnetic resonance imaging apparatus (hereinafter referred to as an MRI (Magnetic Resonance Imaging) apparatus) will be described as an example of a medical image diagnostic apparatus according to the present invention. In the second embodiment, an example of an image processing apparatus according to the present invention will be described. In the third embodiment, other embodiments will be described. In addition, this invention is not limited by the following examples. For example, the medical image diagnostic apparatus may be an X-ray CT (Computed Tomography) apparatus, an ultrasonic diagnostic apparatus, a nuclear medicine diagnostic apparatus, or the like.

[Configuration of MRI system]
First, the configuration of the MRI apparatus 100 according to the first embodiment will be described with reference to FIGS. FIG. 1 is a block diagram illustrating the configuration of the MRI apparatus 100 according to the first embodiment. As shown in FIG. 1, the MRI apparatus 100 according to the first embodiment includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power source 3, a bed 4, a bed control unit 5, a transmission coil 6, A transmission unit 7, reception coils 8 a to 8 e, a reception unit 9, and a computer system 10 are provided. As will be described later, the MRI apparatus 100 according to the first embodiment performs image processing of a plurality of image data having different density distribution characteristics in the control unit 17 included in the computer system 10.

  The static magnetic field magnet 1 is formed in a hollow cylindrical shape and generates a uniform static magnetic field in an internal space. The static magnetic field magnet 1 is, for example, a permanent magnet or a superconducting magnet. The gradient coil 2 is formed in a hollow cylindrical shape and generates a gradient magnetic field in the internal space. Specifically, the gradient magnetic field coil 2 is arranged inside the static magnetic field magnet 1 and receives a current supplied from the gradient magnetic field power supply 3 to generate a gradient magnetic field. The gradient magnetic field power supply 3 supplies current to the gradient magnetic field coil 2 in accordance with the pulse sequence execution data sent from the computer system 10.

  The bed 4 includes a top plate 4a on which the subject P is placed, and the top plate 4a is inserted into the cavity (imaging port) of the gradient magnetic field coil 2 with the subject P placed thereon. Usually, the bed 4 is installed such that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 1. The couch controller 5 drives the couch 4 to move the couchtop 4a in the longitudinal direction and the vertical direction.

  The transmission coil 6 generates a high frequency magnetic field. Specifically, the transmission coil 6 is arranged inside the gradient magnetic field coil 2 and receives a supply of a high frequency pulse from the transmission unit 7 to generate a high frequency magnetic field. The transmission unit 7 transmits a high-frequency pulse corresponding to the Larmor frequency to the transmission coil 6 according to the pulse sequence execution data transmitted from the computer system 10.

  The receiving coils 8a to 8e receive MR (Magnetic Resonance) signals. Specifically, the receiving coils 8a to 8e are arranged inside the gradient magnetic field coil 2 and receive MR signals radiated from the subject P due to the influence of the high-frequency magnetic field. The receiving coils 8 a to 8 e output the received MR signal to the receiving unit 9. For example, the receiving coil 8a is a receiving coil for the head mounted on the head of the subject P. The reception coils 8b and 8c are spine reception coils arranged between the back of the subject P and the top plate 4a, respectively. The receiving coils 8d and 8e are abdominal receiving coils that are attached to the abdomen of the subject.

  The receiving unit 9 generates MR signal data based on the MR signals output from the receiving coils 8a to 8e in accordance with the pulse sequence execution data sent from the computer system 10. Specifically, the receiver 9 generates MR signal data by digitally converting the MR signals output from the receiving coils 8 a to 8 e, and transmits the generated MR signal data to the computer system 10.

  The computer system 10 performs overall control of the MRI apparatus 100, collection of MR signal data, image reconstruction, and the like. The computer system 10 includes an interface unit 11, a data collection unit 12, a data processing unit 13, a storage unit 14, a display unit 15, an input unit 16, and a control unit 17.

  The interface unit 11 is connected to the gradient magnetic field power supply 3, the bed control unit 5, the transmission unit 7, and the reception unit 9, and controls input / output of data transmitted / received between the connected units and the computer system 10. The data collection unit 12 collects MR signal data transmitted from the reception unit 9. When collecting the MR signal data, the data collecting unit 12 sends the collected MR signal data to the data processing unit 13.

  The data processing unit 13 generates image data from the MR signal data sent from the data collection unit 12 and stores the generated image data in the storage unit 14. For example, the data processing unit 13 reconstructs volume data of the morphological image from the MR signal data, and stores the volume data of the reconstructed morphological image in the storage unit 14. For example, the data processing unit 13 reconstructs the function image volume data from the MR signal data, and stores the reconstructed function image volume data in the storage unit 14. For example, the data processing unit 13 generates volume data of a k-space image from the MR signal data, and stores the generated volume data of the k-space image in the storage unit 14.

  The storage unit 14 stores the image data stored by the data processing unit 13. For example, the storage unit 14 is a semiconductor memory device such as a random access memory (RAM), a read only memory (ROM), or a flash memory, a hard disk, an optical disk, or the like. The storage unit 14 will be described in detail later.

  The display unit 15 displays a combined histogram output by a combined histogram output unit 17f described later. For example, the display unit 15 is a display device such as a liquid crystal display. The input unit 16 receives display parameter adjustment instructions and the like from the operator. For example, the input unit 16 is a pointing device such as a mouse or a trackball, a selection device such as a mode switch, or an input device such as a keyboard.

  The control unit 17 comprehensively controls the MRI apparatus 100 by controlling the above-described units. For example, the control unit 17 is an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array), or an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The control unit 17 will be described in detail below.

  Next, detailed configurations of the storage unit 14 and the control unit 17 will be described. FIG. 2 is a functional block diagram illustrating detailed configurations of the storage unit 14 and the control unit 17. As shown in FIG. 2, the storage unit 14 in the first embodiment has a volume data storage unit 14a, in particular.

  The volume data storage unit 14a stores volume data. Specifically, the volume data storage unit 14 a stores the volume data generated by the data processing unit 13 by being stored by the data processing unit 13. The volume data stored in the volume data storage unit 14a is used for processing by the volume data load unit 17a described later.

  FIG. 3 is a diagram for explaining the volume data stored in the volume data storage unit 14a. For example, the volume data storage unit 14a stores volume data illustrated in FIG. (A), (B), and (C) of FIG. 3 are volume data of a morphological image, a functional image, and a k-space image, respectively, whose imaging targets match, and are image data having different density distribution characteristics. Here, the density distribution characteristics represent what values of pixels on the image are distributed where and how, and the characteristics differ depending on the collection conditions of each image.

  Returning to FIG. 2, the control unit 17 in the first embodiment particularly includes a volume data load unit 17 a, a volume data alignment unit 17 b, a mapping order designation reception unit 17 c, a density histogram creation unit 17 d, and a mapping data creation unit. 17e, a combined histogram output unit 17f, a display parameter reception unit 17g, and a rendering image creation unit 17h.

  The volume data load unit 17a loads a plurality of volume data into the memory. Specifically, the volume data load unit 17a refers to the volume data storage unit 14a, reads a plurality of volume data to be subjected to image processing, and loads them into a memory (a memory included in the control unit 17, not shown). . The plurality of volume data loaded in the memory is used for processing by the volume data alignment unit 17b. For example, the volume data loading unit 17a loads the volume data of the morphological image, the volume data of the functional image, and the volume data of the k-space image illustrated in FIG. 3 into the memory upon receiving an instruction from the operator. To do.

  The volume data alignment unit 17b aligns the spatial positions of a plurality of volume data. Specifically, the volume data alignment unit 17b aligns the spatial positions of the plurality of volume data loaded in the memory using a known technique, and maps the plurality of registered volume data to the mapping order designation receiving unit 17c. Output to. For example, the volume data alignment unit 17b aligns the anatomical positions of the volume data of the morphological image, the volume data of the functional image, and the volume data of the k-space image illustrated in FIG. 3 using a known technique.

  The mapping order designation receiving unit 17c accepts designation of an order for mapping density distributions. Here, the volume data designated in the mapping order 1 is a target for which a density histogram is created by the density histogram creation unit 17d, and the volume data designated in the mapping order 2 or later is a target to be mapped on this density histogram. It becomes.

  For example, when the mapping order designation receiving unit 17c receives a plurality of registered volume data from the volume data positioning unit 17b, the mapping order designation receiving unit 17c displays the received volume data on the display unit 15. For example, the mapping order designation receiving unit 17c displays on the display unit 15 the volume data of the morphological image, the volume data of the functional image, and the volume data of the k-space image exemplified in FIG. . For example, when the mapping order designation receiving unit 17c receives the designation of the mapping order from the operator, the mapping order designation unit 17c associates each volume data with the mapping order.

  Further, for example, the mapping order designation receiving unit 17c outputs the volume data designated in the mapping order 1 to the density histogram creating unit 17d, and the volume data designated in the mapping order 2 or later to the mapping data creating unit 17e. Output. For example, the mapping order designation receiving unit 17c outputs the volume data of the morphological image designated in the mapping order 1 to the density histogram creating unit 17d. Further, for example, the mapping order designation receiving unit 17c outputs the volume data of the functional image and the volume data of the k-space image designated after the mapping order 2 to the mapping data creation unit 17e.

  In the first embodiment, the method in which the mapping order designation receiving unit 17c receives the designation of the mapping order from the operator has been described, but the present invention is not limited to this. For example, by pre-defining a rule such as “when volume data includes a morphological image, the volume data of the morphological image is designated as mapping order 1”, the method of automatically specifying the mapping order is also used. The present invention can be similarly applied.

  The density histogram creation unit 17d classifies the pixels of the volume data designated in the mapping order 1 for each density value divided at a predetermined interval, and creates a density histogram. For example, when the density histogram creating unit 17d receives the volume data designated in the mapping order 1 from the mapping order designation receiving unit 17c, the density histogram creating unit 17d creates a density histogram data table from the received volume data and stores the density histogram data table in the memory. (Or storage unit 14). Further, for example, the density histogram creation unit 17 d reads the data table stored in the memory and displays the density histogram of the volume data designated in the mapping order 1 on the display unit 15.

  Here, the density histogram is a graph representing the appearance frequency of the density value of a pixel. In general, the horizontal axis is the density value divided at a predetermined interval, and the vertical axis is the appearance frequency (number of pixels). FIG. 4 is a diagram for explaining the density histogram. 4A is an example of a density histogram, and FIG. 4B is a graph obtained by converting the edge of the density histogram shown in FIG. 4A into a curve.

  FIG. 5 is a diagram showing a data table of density histograms and mapping data. For example, the density histogram creation unit 17d creates a column of “volume data of mapping order 1” in the data table illustrated in FIG. 5 and stores it in the memory. For example, the density histogram creation unit 17d reads the column of “volume data of mapping order 1” stored in the memory, and displays the density histogram illustrated in FIG.

  For example, when the density histogram creation unit 17d receives the volume data designated in the mapping order 1 from the mapping order designation acceptance unit 17c, the density histogram creation unit 17d analyzes the density values of the pixels and classifies them for each density value divided at a predetermined interval. . For example, the density histogram creating unit 17d classifies the density values into “P1”, “P2”,..., “PN”, and the pixels of the volume data as “P1”, “P2”,. ] Is classified. For example, “P1” is a density value classification such as “−1520 to −1395”.

Next, for example, the density histogram creation unit 17d counts the number of pixels classified for each density value as the appearance frequency. For example, the density histogram creation unit 17d counts the number of pixels divided into “P1”, the number of pixels divided into “P2”,..., The number of images classified into “PN” as appearance frequencies. To do. Then, for example, the density histogram creating unit 17d associates the density value classification, the appearance frequency, and the position information of the pixels classified in this classification and stores them in the “volume data of mapping order 1” column. For example, the density histogram creation unit 17d associates “P1”, the appearance frequency “x ₁ ”, and the coordinates of the pixels classified into “P1” and stores them in the “volume data of mapping order 1” column. To do. The coordinates of the image are stored for the number of pixels “x ₁ ” divided into “P1”.

  The mapping data creation unit 17e creates mapping data for the volume data specified after the mapping order 2. For example, when the mapping data creation unit 17e receives the volume data designated after the mapping order 2 from the mapping order designation receiving unit 17c, the mapping data creation unit 17e creates a mapping data data table from the received volume data, and creates the mapping data data table. Store in the memory (or storage unit 14). When the mapping data creation unit 17e stores the mapping data data table in the memory for all the volume data designated after the mapping order 2, the mapping data creation unit 17e notifies the composite histogram output unit 17f that the mapping data has been created.

  For example, the mapping data creation unit 17e creates a column of “volume data of mapping order N” in the data table illustrated in FIG. 5 and stores it in the memory. In the data table illustrated in FIG. 15, only the column of “volume data in mapping order N” is illustrated, but if there is a plurality of volume data after mapping order 2, “volume data in mapping order N” There are also multiple columns.

  For example, when the mapping data creating unit 17e receives the volume data designated in the mapping order 2 from the mapping order designation receiving unit 17c, the mapping data creating unit 17e classifies the pixels of “volume data designated in the mapping order 2”. For example, the mapping data creation unit 17e classifies pixels existing in the same spatial position as the pixels of “volume data specified in mapping order 1” classified into the same division by the density histogram creation unit 17d. As described above, classification is performed using pixel position information.

  For example, the mapping data creation unit 17e refers to the “volume data of mapping order 1” column of the data table illustrated in FIG. 5 and classifies “P1”, “P2”,. Reference is made to pixel position information. Then, the mapping data creation unit 17e classifies the pixels of “volume data specified in the mapping order 2” so that the pixels having the same position information as the position information are classified into the same division.

  Further, for example, the mapping data creating unit 17e classifies the pixel groups of “volume data of mapping order 2” classified into the same segment for each density value segmented at a predetermined interval. For example, the mapping data creation unit 17e classifies the density values into “P′1”, “P′2”,..., “P′N”, and sets the volume data pixels to “P′1”, “P ′”. “2”,..., “P′N”.

Further, for example, the mapping data creation unit 17e counts the number of pixels classified for each density value as the appearance frequency. For example, the mapping data creation unit 17e may determine the number of pixels divided into “P′1”, the number of pixels divided into “P′2”,..., The image divided into “P′N”. Count the number as an appearance frequency. Then, for example, the mapping data creation unit 17e associates the pixel position information, the density value classification, and the appearance frequency with each other and stores them in the “volume data in mapping order 2” column. For example, the mapping data creation unit 17e is configured such that the pixel coordinates, “P′1”, “P′2”,... “P′N”, appearance frequencies “y ₁₁ ”, “y ₂₁ ”,. -“Y _N1 ” is associated and stored in the “volume data of mapping order 2” column. The coordinates of the image are stored for the number of pixels “x ₁ ” divided into “P1” of “volume data of mapping order 1”.

  The composite histogram output unit 17 f creates a composite histogram obtained by mapping the mapping data on the density histogram and displays it on the display unit 15. For example, the composite histogram output unit 17f reads the data table stored in the memory by the mapping data creation unit 17e, creates a bar graph-shaped density histogram, and superimposes it on the density histogram created by the density histogram creation unit 17d. indicate.

  FIG. 6 is a diagram for explaining the combined histogram. For example, the density histogram creation unit 17d has already appeared, the density value of the volume data of the mapping order 1 with the horizontal axis partitioned at a predetermined interval, and the appearance of the pixel of the volume data of the mapping order 1 classified with the same vertical axis. A density histogram is displayed as the frequency (number of pixels).

Here, for example, the combined histogram output unit 17f displays, on the bar graph displayed along the vertical axis, the pixel of the volume data in the mapping order 2 and the pixel existing at the same spatial position as the pixel of the volume data in the mapping order 1. The density histograms are arranged in order from the bottom to the top, from the smallest density value to the largest density value. By displaying in this way, the characteristic density distribution possessed by the volume data of mapping order 2 can be mapped onto the density histogram of the volume data of mapping order 1. In other words, when the images are not arranged and displayed in this order, the density distributions are displayed in a dispersed manner, making it difficult to understand the characteristic density distribution of the volume data in mapping order 2. Note that the maximum value of the vertical axis indicated by the bar graph (the number of pixels classified in the same section) is the same as the value indicated by the volume data in mapping order 1. This will be explained using the data table of FIG. 5. For example, it means that the appearance frequency “x ₁ ” matches the sum of the appearance frequencies “y ₁₁ ” to “y _N1 ”.

  Further, for example, when the density histogram of the volume data in the mapping order 2 is superimposed and displayed on the density histogram of the volume data in the mapping order 1, the composite histogram output unit 17f displays the color classification for each density value. For example, the composite histogram output unit 17f displays the density histogram of the volume data of the mapping order 2 with the same brightness and color as the brightness and color when the volume data of the mapping order 2 is displayed on the display unit 15.

  For example, the composite histogram output unit 17f displays a bar graph in the same manner for the volume data after the mapping order 3. For example, when synthesizing the mapping data related to the volume data of the mapping order 3, the synthesis histogram output unit 17f uses an arbitrary calculation formula for the value on the bar graph that has already been synthesized and displayed, and the newly obtained value on the bar graph. To synthesize. For example, any calculation method may be used, such as addition average, maximum value, minimum value, and standard deviation. For example, there is also a method of performing synthesis by specifying a synthesis ratio. For example, a method may be used in which the mapping order 2 is specified as 70% and the mapping order 3 is specified as 30%.

  The display parameter receiving unit 17g receives a display parameter adjustment instruction. For example, the display parameter receiving unit 17g displays a bar for adjusting the window width and the window level on the combined histogram displayed on the display unit 15 by the combined histogram output unit 17f. For example, when the display parameter receiving unit 17g receives an operation on the bar from the operator, the display parameter receiving unit 17g notifies the rendering image creating unit 17h of the window width and the window level adjusted by the operator.

  FIG. 7 is a diagram for explaining adjustment of the window width and the window level. Here, the window width is a density value range assigned to the range from the maximum brightness to the minimum brightness, and the window level is a density value located in the middle of the window width. For example, when the slope of the bar is changed to a steeper slope by the operator and the window width is narrowed, the contrast increases for the density value region within the window width range. On the other hand, when the window level is raised or lowered by the operator, the entire image becomes brighter or darker.

  Here, as described above, the combined histogram output unit 17f displays the density histogram of the volume data with the same luminance and color as the luminance and color when the volume data is displayed on the display unit 15. For this reason, for example, when the display parameter receiving unit 17g receives a change in the window width and window level for displaying the volume data of the mapping order 2 on the display unit 15, it is displayed on the display unit 15 by the composite histogram output unit 17f. Also for the combined histogram, the brightness and color are changed with the change of the window width and the window level.

  That is, for example, for each volume data, the control unit 17 holds the window width and window level when displaying the volume data on the display unit 15. For example, the control unit 17 holds the initial values of the window width and window level, and changes the held window width and window level values when receiving an adjustment instruction by the display parameter receiving unit 17g. On the other hand, when the composite histogram output unit 17f displays the composite histogram, the composite histogram output unit 17f acquires the window width and the window level for each volume data held in the control unit 17, and the brightness and color according to the acquired window width and window level. Create an assignment and density histogram. For this reason, a change in contrast on the image is also reflected in the density distribution information displayed on the combined histogram, while a change in contrast on the combined histogram is also reflected in the image at the same time.

  The rendering image creation unit 17h creates a rendering image. Specifically, the rendering image creation unit 17h creates a rendering image using the display parameters notified from the display parameter reception unit 17g, and displays the created rendering image on the display unit 15.

[Processing procedure by MRI equipment]
Next, a processing procedure performed by the MRI apparatus 100 according to the first embodiment will be described with reference to FIG. FIG. 8 is a flowchart illustrating a processing procedure performed by the MRI apparatus 100 according to the first embodiment. As shown in FIG. 8, in the MRI apparatus 100, the volume data loading unit 17a loads a plurality of volume data into the memory, for example, triggered by receiving an instruction from the operator (step S101).

  Next, the volume data alignment unit 17b aligns the spatial positions of the plurality of volume data (step S102). Subsequently, the mapping order designation receiving unit 17c determines whether or not the designation of the order for mapping the density distribution is accepted (step S103).

  If it is determined that the mapping order designation accepting unit 17c has accepted (Yes at Step S103), then the density histogram creating unit 17d then separates the pixels of the volume data designated in the mapping order 1 for each density value divided at a predetermined interval. Classification is performed and a density histogram is created and displayed on the display unit 15 (step S104).

  Subsequently, the mapping data creation unit 17e determines whether there is unprocessed volume data (step S105). If it is determined that there is unprocessed volume data (Yes in step S105), mapping data is created (step S105). Steps S106 to S107).

  Specifically, the mapping data creation unit 17e first determines, for the mapping order N volume data, a pixel that exists at the same spatial position as the mapping order 1 volume data pixel as the mapping order 1 volume data classification. It classify | categorizes into the same division (step S106). Next, the mapping data creation unit 17e creates mapping data by classifying the pixel groups classified into the same classification for each density value (step S107).

  On the other hand, if the mapping data creation unit 17e determines in step S105 that there is no unprocessed volume data (No in step S105), the combined histogram output unit 17f then generates a combined histogram obtained by mapping the mapping data to the density histogram. It creates and displays on the display part 15 (step S108).

  The above processing procedure is merely an example, and the present invention is not limited to the above processing procedure. For example, in the above processing procedure, it has been described that the density histogram creation unit 17d displays the density histogram on the display unit 15 in step S104, but the present invention is not limited to this. For example, the density histogram may not be displayed in step S104, and for the first time in step S108, the combined histogram output unit 17f may display the density histogram related to the volume data in mapping order 1. Alternatively, the density histogram relating to the volume data of mapping order 1 may not be displayed.

[Effect of Example 1]
As described above, in the MRI apparatus 100 according to the first embodiment, the density histogram creation unit 17d classifies the pixels of the first image for each density value divided at a predetermined interval. In addition, the mapping data creation unit 17e classifies the pixels of the second image with the pixels in the same spatial position as the pixels of the first image classified into the same segment by the density histogram creation unit 17d. The pixel groups of the second image classified in the same division are classified for each density value divided at a predetermined interval. The combined histogram output unit 17g outputs the classification result classified by the mapping data creation unit 17e.

  For this reason, the MRI apparatus 100 according to the first embodiment can simplify the operation for adjusting the display parameters. That is, according to the MRI apparatus 100 according to the first embodiment, the classification result regarding the first image is reflected in the mapping data that is the classification result regarding the second image. For this reason, the MRI apparatus 100 simply outputs the classification result for the second image as a combined histogram, and outputs the density distribution characteristics for both images at the same time. As a result, the display parameters for adjusting both images are adjusted. The operation can be performed on one screen.

  That is, according to the MRI apparatus 100 according to the first embodiment, by analyzing a plurality of volume data pixels having different density distribution characteristics from each other according to a certain rule, the analysis result is displayed on one screen and one horizontal axis. It can be easily expressed on one shared graph. As a result, the operator can not only grasp the relationship between the density distributions at a glance, but also more accurately and easily perform an operation for handling a plurality of image data. Compared with the conventional operability, the operability is dramatically improved.

  Here, for example, it is assumed that a characteristic density distribution appears in a certain section of a bar graph on the combined histogram. The lesion part depicted in the morphological image and the lesion part depicted in the functional image are present at the same spatial position. In general, it is considered that the lesion area has a similar density value in the same image and exhibits a characteristic density distribution. If this is the case, it means that the characteristic density distribution appearing on the composite histogram corresponds to both the lesion area depicted in the morphological image and the lesion area depicted in the functional image. Therefore, the operator simply operates to set an optimal display parameter for the characteristic density distribution displayed on one composite histogram, and is optimal for both morphological images and functional images having different density distribution characteristics. Display parameters can be adjusted.

  In addition, according to the first embodiment, the change in contrast on the image is simultaneously reflected in the density distribution information displayed on the combined histogram, while the change in contrast on the combined histogram is simultaneously reflected in the image. Is done. For this reason, for example, when the operator adjusts the contrast of the affected lesion part while viewing the image, the contrast is similarly adjusted in a region having the density histogram on the composite histogram. As a result, the operator can easily determine that the region in which the contrast is adjusted corresponds to the lesioned part on the combined histogram.

  In addition, for example, the morphological image and the functional image have different contrasts. In general, it is difficult to specify the lesioned part on the functional image and adjust the contrast. On the other hand, if it is on a morphological image, it is relatively easy to identify a lesion and adjust the contrast. Here, in the composite histogram, pixel information of the functional image existing at the same spatial position as the pixel of the morphological image is superimposed on the pixel information of the morphological image. For this reason, in the composite histogram, the region in which the contrast is adjusted by the morphological image corresponds to a lesion on the functional image, and the operator can adjust the contrast of the functional image on the composite histogram. Becomes easier.

  In the first embodiment, an example of an MRI apparatus has been described as an example of the medical image diagnostic apparatus according to the present invention. In the second embodiment, an example of an image processing apparatus according to the present invention will be described. FIG. 9 is a block diagram illustrating the configuration of the image processing apparatus 200 according to the second embodiment.

[Configuration of Image Processing Apparatus According to Second Embodiment]
As illustrated in FIG. 9, the image processing apparatus 200 according to the second embodiment includes a communication unit 210, an input unit 211, an output unit 212, an input / output I / F (Interface) unit 213, a storage unit 220, And a control unit 230.

  The communication unit 210 is a general interface for IP (Internet Protocol) communication, for example, and performs communication with other devices connected to the network, medical image diagnostic devices, and the like. The input unit 211 receives a display parameter adjustment instruction or the like from the operator. For example, the input unit 211 is a keyboard or a mouse. The output unit 212 displays a composite histogram output by a composite histogram output unit 230f described later. For example, the output unit 212 is a display or the like. The input / output control I / F unit 213 controls input / output between the input unit 211, the output unit 212, the storage unit 220, and the control unit 230.

  The storage unit 220 is, for example, a semiconductor memory device such as a RAM, a ROM, or a flash memory, a hard disk, an optical disk, and the like, and stores various types of information. Specifically, the storage unit 220 includes an image data storage unit 220a as shown in FIG.

  The image data storage unit 220a stores a plurality of image data having different density distribution characteristics. For example, the image data storage unit 220a stores image data input via the communication unit 210 or the input unit 211. The image data stored in the image data storage unit 220a is used for processing by the image data load unit 230a described later.

  The control unit 230 controls various processes executed in the image processing apparatus 200. As shown in FIG. 9, the image data loading unit 230a, the image data alignment unit 230b, the mapping order designation receiving unit 230c, and the density A histogram creation unit 230d, a mapping data creation unit 230e, a combined histogram output unit 230f, a display parameter reception unit 230g, and a display parameter adjustment image creation unit 230h are included.

  The image data loading unit 230a refers to the image data storage unit 220a and loads a plurality of image data into a memory (a memory included in the control unit 230, not shown). The image data alignment unit 230b aligns the spatial positions of the plurality of image data loaded in the memory using a known technique, and outputs the plurality of registered image data to the mapping order designation receiving unit 230c.

  The mapping order designation accepting unit 230c accepts designation of the order in which the density distribution is mapped, outputs the image data designated in the mapping order 1 to the density histogram creation unit 230d, and the image data designated after the mapping order 2 Is output to the mapping data creation unit 230e.

  When receiving the image data designated in the mapping order 1 from the mapping order designation accepting unit 17c, the density histogram creating unit 230d classifies the pixels of the image data for each density value divided at a predetermined interval, and creates a density histogram. . When the mapping data creation unit 230e receives image data designated after the mapping order 2 from the mapping order designation accepting unit 17c, the mapping data creation unit 230e creates mapping data from the image data. Specifically, the mapping data creation unit 230e assigns the pixels of the image data designated in the mapping order 1 that are classified into the same division by the density histogram creation unit 230d. The pixels in the same spatial position are classified so as to be classified into the same division. Further, the mapping data creation unit 230e classifies the pixel groups classified into the same segment for each density value segmented at a predetermined interval.

  The synthesized histogram output unit 230f creates a synthesized histogram obtained by mapping the mapping data on the density histogram and displays it on the output unit 212. The display parameter receiving unit 230g displays a bar for adjusting the window width and the window level on the combined histogram displayed on the output unit 212 by the combined histogram output unit 230f. Further, when receiving an operation on the bar from the operator, the display parameter receiving unit 230g notifies the display parameter adjustment image creating unit 230h of the window width and the window level adjusted by the operator. The display parameter adjustment image creation unit 230h creates an image using the display parameter notified from the display parameter reception unit 230g, and displays the created image on the output unit 212.

[Effect of Example 2]
As described above, in the image processing apparatus 200 according to the second embodiment, the density histogram creation unit 230d classifies the pixels of the first image for each density value divided at a predetermined interval. In addition, the mapping data creation unit 230e classifies the pixels of the second image into pixels having the same spatial position as the pixels of the first image classified into the same segment by the density histogram creation unit 230d. The pixel groups of the second image classified in the same division are classified for each density value divided at a predetermined interval. Further, the combined histogram output unit 230f outputs the classification result classified by the mapping data creation unit 230e.

  For this reason, the image processing apparatus 200 according to the second embodiment can simplify the operation for adjusting the display parameters. That is, according to the image processing apparatus 200 according to the second embodiment, the classification result regarding the first image is reflected in the mapping data that is the classification result regarding the second image. Therefore, the image processing apparatus 200 outputs the density distribution characteristics regarding both images at the same time only by outputting the classification result regarding the second image as a composite histogram, and as a result, adjusts the display parameters regarding both images. This operation can be performed on one screen.

  In other words, according to the image processing apparatus 200 according to the second embodiment, by analyzing a plurality of image data pixels having different density distribution characteristics from each other according to a certain rule, the analysis result is displayed on one screen and one horizontal axis. Can be expressed in an easy-to-understand manner on one graph that shares As a result, the operator can not only grasp the relationship between the density distributions at a glance, but also more accurately and easily perform an operation for handling a plurality of image data. Compared to conventional operability, it can be said that operability is dramatically improved.

  Note that the image processing apparatus 200 according to the second embodiment can handle various image data as the image data. The image processing apparatus 200 can handle medical image data such as a CT image, an ultrasonic image, a PET (Positron Emission Tomography) image, and a SPECT (Single Photon Emission Computed Tomography) image. The image processing apparatus 200 can also handle other general image data.

  In addition, the present invention may be implemented in various different forms other than the above-described embodiments.

[Use composite histogram]
In the above embodiment, the composite histogram displayed on the display by the MRI apparatus 100 or the image processing apparatus 200 has been described as being mainly used for adjusting display parameters. However, the present invention is not limited to this.

  10 and 11 are diagrams for explaining the use of the combined histogram. For example, it is assumed that the MRI apparatus 100 has created a composite histogram of a morphological image, a functional image, and a k-space image. Here, as described with reference to FIG. 5, in the first embodiment, the density histogram related to the volume data in the mapping order 1 and the density histogram related to the volume data in the mapping order N are associated by the pixel position information. Yes.

  Therefore, for example, if the MRI apparatus 100 accepts a click on a certain segment of a certain bar graph on the composite histogram as shown in FIG. 10, the click is “volume data of mapping order N” shown in FIG. You will specify a row in the column. Here, as shown in FIG. 5, a certain row in the column of “volume data of mapping order N” includes the position information of the pixels of the volume data of mapping order N and the position of the pixels of the volume data of mapping order 1 Associated with information. As a result, the MRI apparatus 100 can grasp which position on the original morphological image, the functional image, and the k-space image corresponds to the place where the click is received on the composite histogram. As shown, for example, the position can be explicitly displayed.

  Further, for example, as shown in FIG. 11, the MRI apparatus 100 explicitly displays the position, for example, and displays the image more emphasized by lowering the brightness of other areas of the image as a whole. You can also

  In FIG. 5, the position information is stored in association with a plurality of rows “P′1” to “P′N”, but the present invention is not limited to this, and “P′1” is not limited thereto. Position information may be stored in units of. In this case, the MRI apparatus 100 can more precisely display the position on the composite histogram corresponding to the position where the click is received on the original morphological image, functional image, and k-space image. it can.

[Automation of display parameter adjustment]
In the above-described embodiment, the composite histogram created by the MRI apparatus 100 and the image processing apparatus 200 has been described as being mainly used for adjustment of display parameters by the operator. However, the present invention is not limited to this.

  For example, the MRI apparatus 100 and the image processing apparatus 200 may simply store, for example, data illustrated in FIG. 5 in the storage unit as data for adjusting the display parameters without displaying the generated composite histogram. . In this case, the MRI apparatus 100 and the image processing apparatus 200 analyze the data stored in the storage unit and, for example, determine the correspondence between the characteristic density distribution, density value, and position information. Then, the MRI apparatus 100 and the image processing apparatus 200 calculate optimal display parameters using the determined position information and density values. Thus, by automating the adjustment of the display parameters, it is possible to greatly reduce the time and the number of operation steps required to display the target image.

  In addition, since the density distribution of the data to be mapped can be seen at a glance on the histogram, the position specified on the histogram can be used if you want to match the contrast to a portion where a certain density value is fixed on the histogram. The display conditions can be changed according to. For example, an image display condition is specified under a certain condition with respect to a density value present at a specified position on the histogram. Also, for example, the window width value is set around the designated position. It is also possible to obtain connectivity from the neighborhood density value existing in the designated portion, and to cut out a part of the mapped image data existing at the same position as this connected region.

DESCRIPTION OF SYMBOLS 100 MRI apparatus 17 Control part 17a Volume data load part 17b Volume data alignment part 17c Mapping order designation | designated reception part 17d Density histogram creation part 17e Mapping data creation part 17f Composite histogram output part 17g Display parameter reception part 17h Rendering image creation part

Claims (5)

First classification means for classifying the pixels of the first image for each density value divided at a predetermined interval;
Pixels in the second image having density distribution characteristics different from those in the first image are classified into pixels in the same spatial position as the pixels in the first image classified in the same classification by the first classification means. A second classification means for classifying the pixel groups of the second image classified into the same classification for each density value classified at a predetermined interval;
An image processing apparatus comprising: output means for outputting a classification result classified by the second classification means.   The image processing apparatus according to claim 1, further comprising an accepting unit that accepts an adjustment instruction for instructing adjustment of display conditions relating to the first image and the second image.   3. The output means according to claim 1, wherein the output means superimposes and outputs a bar graph indicating the classification result by the second classification means on a density histogram indicating the classification result by the first classification means. Image processing device. The first image and the second image are medical images captured by a medical image capturing device,
Image receiving means for receiving the input of the first image and the second image from the medical imaging device;
The first classifying unit classifies the pixels of the first image received by the image receiving unit,
The image processing apparatus according to claim 1, wherein the second classifying unit classifies pixels of the second image received by the image receiving unit. First image capturing means for capturing the first image;
A second image capturing means for capturing a second image having a density distribution characteristic different from that of the first image;
First classification means for classifying the pixels of the first image imaged by the first image imaging means for each density value divided at a predetermined interval;
The pixels of the second image picked up by the second image pickup means are classified into the same division by the pixels present in the same spatial position as the pixels of the first image classified into the same division by the first classification means. A second classification means for classifying the pixel groups of the second image classified into the same classification for each density value classified at a predetermined interval;
And a medical image diagnostic apparatus comprising: output means for outputting a classification result classified by the second classification means.