JP2005294932A - Image processing method, image processing apparatus and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enable processing an image by a simple operation and suitable processing parameters, even if the details of image processing contents are not known. <P>SOLUTION: This apparatus has a parameter input means for accepting the input of one or a plurality of input parameters predetermined on the basis of a measure different from the image processing parameter, a parameter converting means for converting the input parameter inputted by the parameter input means into one or a plurality of image processing parameters to be used for image processing on the basis of predetermined relation, and an image processing means for executing image processing using one or the plurality of image processing parameters converted by the parameter converting means. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image processing method, an image processing apparatus, and an image processing program for processing a radiographic image, and more specifically, an image processing method and an image processing apparatus capable of obtaining a radiographic image suitable for diagnosis or the like by a simple operation. And an image processing program.

  In recent years, an apparatus capable of directly capturing a radiographic image as a digital image has been developed. For example, as a device for detecting a radiation dose applied to a subject and obtaining a radiographic image formed in accordance with the detected dose as an electrical signal, a method using a detector using a stimulable phosphor is disclosed in JP Many publications such as 55-12429 and JP 63-189853 are disclosed.

  In such an apparatus, a stimulable phosphor is applied to a sheet-like substrate or fixed to the detector by vapor deposition or the like, and once irradiated with radiation transmitted through the subject, the stimulable phosphor absorbs the radiation.

  Thereafter, the stimulable phosphor is excited by light or heat energy, and the stimulating phosphor emits radiation energy accumulated by the absorption as fluorescence, and the fluorescence is photoelectrically converted. An image signal is obtained.

  On the other hand, a charge corresponding to the intensity of irradiated radiation is generated in the photoconductive layer, the generated charge is accumulated in a plurality of capacitors arranged two-dimensionally, and the accumulated charges are taken out. A radiation image detection apparatus has been proposed.

  Such a radiation image detection apparatus uses what is called a flat panel detector (FPD). As described in JP-A-9-90048, this type of FPD includes a phosphor that emits fluorescence corresponding to the intensity of irradiated radiation, and fluorescence emitted from the phosphor directly or through a reduction optical system. Known is a combination of a photodiode that receives light and performs photoelectric conversion, or a combination of photoelectric conversion elements such as a CCD.

In addition, as described in Japanese Patent Laid-Open No. 6-342098, there is also known one that directly converts irradiated radiation into electric charges.
In these radiological image detection apparatuses, it is common to perform image processing such as gradation conversion processing and edge enhancement processing on an acquired image so that the image is suitable for diagnosis.

  Note that when displaying or outputting a radiographic image based on the image data obtained in this way, image processing is performed so that the radiographic image is easy to see without being subjected to fluctuations in imaging conditions.

  Therefore, after obtaining a cumulative histogram of image data in a desired region of a radiographic image, image processing is performed by setting a data level at which the cumulative histogram is a predetermined ratio as a reference signal value. It is described in Document 1.

Patent Document 2 below describes that image processing conditions are determined and appropriate image processing is performed based on the distribution state of the high signal value region and the low signal value region.
JP-A-6-61325 (first page, FIG. 1) JP 2000-1575187 A (pages 1 to 5, FIG. 6)

  For example, the image processing condition of the chest image in the conventional process is determined as follows. Here, for example, the positions of the left and right ends of the rib cage to be recognized can be adjusted by making k1 and k2 that determine the threshold values Tr and Tl changeable. Further, for example, by making it possible to change the threshold values m1 and m2 for determining the reference signal value from the cumulative histogram, the signal value in the lung field to be detected can be changed. For example, when performing gradation processing or frequency processing, if a desired contrast is to be obtained, it can be obtained by increasing the contrast of the gradation characteristics, or the contrast can be obtained by frequency processing. Also in the visible range of the image, it is possible to widen the visible range by lowering the gradation characteristics, and it is also possible to widen the visible range by equalization processing.

  As described above, when adjustment with a plurality of parameters is necessary for the same purpose, the operator actually outputs an image, and as a result of comparison, the final parameter is determined by the operator's judgment.

However, in the conventional method, the parameter to be changed is determined from an algorithm for performing the process or a programming element, and thus it is difficult to intuitively understand.
For example, as already mentioned above, as a function for adjusting the image contrast, there is a method of changing the γ value representing the average slope of the LUT by gradation processing. On the other hand, frequency enhancement processing can be considered as a function for adjusting the sharpness of an image. However, frequency enhancement processing affects the contrast of an image. In particular, when emphasizing from low-frequency components, the response of a large component of the image is manipulated, and the influence on the image contrast is not small. Therefore, if the operator adjusts the image contrast with the γ value and then manipulates the frequency enhancement process to adjust the sharpness, it will affect the image contrast and both times until the desired image processing parameters are obtained. The problem arises that the processing of the above must be adjusted.

  For this reason, if the details of the image processing are not known in detail, it has been very difficult to change to an appropriate value. In addition, when multiple parameters are not independent of each other and the optimum parameters can be obtained by adjusting each other to an appropriate value, it is almost impossible to obtain an appropriate value unless the image processing content is fully understood. .

  The present invention has been made in view of the problems as described above, and can perform image processing with appropriate processing parameters with a simple operation even when details of image processing contents are not known. It is an object to realize a method, an image processing apparatus, and an image processing program.

That is, the above-described problems can be solved by the inventions listed below.
(1) The invention according to claim 1 is an image processing method for performing image processing on image data of a radiographic image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters. A parameter input step for receiving input of one or more predetermined input parameters based on a scale different from the parameters, and the input parameters input in the parameter input step are used for image processing based on a predetermined relationship A parameter conversion step for converting the image processing parameter into one or a plurality of image processing parameters, and an image processing step for executing image processing using the one or a plurality of image processing parameters converted in the parameter conversion step. This is an image processing method.

  (2) In the invention described in claim 2, the input parameters are the imaging region, the body position, the shape of the irradiation field, the width of the irradiation field, the importance of the center of the image, the importance of the specific area in the image, and the high frequency in the image. The one or more parameters including at least one of the importance of the component, the importance of the granularity in the area in the image, and the difference from the specified value of the average density in the area in the image. 1. The image processing method according to 1.

  (3) In the invention according to claim 3, the image processing parameter is one or more parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter. The image processing method according to claim 1, wherein the image processing method is an image processing method according to claim 1.

  (4) The invention according to claim 4 is characterized in that in the parameter conversion step, conversion from an input parameter to an image processing parameter is executed based on a decision making theory. The image processing method according to any one of the above.

  (5) The invention described in claim 5 is characterized in that the conversion from the input parameter to the image processing parameter based on the decision making theory in the parameter conversion step is executed based on fuzzy integration. The image processing method described.

  (6) The invention according to claim 6 is an image processing apparatus that performs image processing on image data of a radiographic image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters. Parameter input means for receiving input of one or more predetermined input parameters based on a scale different from the parameters, and the input parameters input by the parameter input means are used for image processing based on a predetermined relationship Parameter converting means for converting into one or more image processing parameters, and image processing means for executing image processing using the one or more image processing parameters converted by the parameter converting means. An image processing apparatus.

  (7) In the invention according to claim 7, the input parameters input by the parameter input means are: an imaging region, an imaging body position, an irradiation field shape, an irradiation field width, an importance at the center of an image, and an in-image specification One or more parameters including at least one of the importance of the area, the importance of the high-frequency component in the image, the importance of the granularity in the area in the image, and the difference from the specified value of the average density in the area in the image The image processing apparatus according to claim 6.

  (8) In the invention according to claim 8, the image processing parameter converted by the parameter conversion means includes at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter. The image processing apparatus according to claim 6, wherein the image processing apparatus has one or more parameters.

  (9) The invention according to claim 9 is characterized in that the parameter conversion means performs conversion from an input parameter to an image processing parameter based on a decision making theory. An image processing apparatus according to any one of the above.

  (10) The invention described in claim 10 is characterized in that the conversion from the input parameter to the image processing parameter based on the decision making theory in the parameter conversion means is executed based on fuzzy integration. It is an image processing apparatus of description.

  (11) The invention according to claim 11 is an image processing program for performing image processing on image data of a radiographic image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters. A parameter input routine for accepting input of one or more predetermined input parameters based on a scale different from the parameters, and the input parameters input in the parameter input routine are used for image processing based on a predetermined relationship A parameter conversion routine for converting into one or a plurality of image processing parameters to be performed, and an image processing routine for performing image processing using the one or a plurality of image processing parameters converted by the parameter conversion routine. This is an image processing program.

  (12) In the invention according to claim 12, the input parameters are the imaging region, the body position, the shape of the irradiation field, the width of the irradiation field, the importance of the center of the image, the importance of the specific area in the image, and the high frequency in the image. The one or more parameters including at least one of the importance of the component, the importance of the granularity in the area in the image, and the difference from the specified value of the average density in the area in the image. 11 is an image processing program.

  (13) In the invention described in claim 13, the image processing parameter is one or a plurality of parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter. 13. An image processing program according to claim 11, wherein the image processing program is any one of claims 11 and 12.

  (14) In the invention described in claim 14, in the parameter conversion routine, conversion from an input parameter to an image processing parameter is executed based on a decision making theory. Any one of the image processing programs.

  (15) The invention described in claim 15 is characterized in that the conversion from the input parameter to the image processing parameter based on the decision making theory in the parameter conversion routine is executed based on fuzzy integration. It is an image processing program of description.

According to the present invention, the effects listed below can be obtained.
(1) In the present invention, when image processing is performed on image data of a radiographic image having a signal corresponding to the radiation dose transmitted through the subject using the image processing parameter, based on a scale different from the image processing parameter. Receiving input of one or more predetermined input parameters, converting the input parameters input in the parameter input step into one or more image processing parameters used for image processing based on a predetermined relationship; Image processing is executed using one or more image processing parameters converted in the parameter conversion step.

  As described above, instead of directly inputting the image processing parameter, one or a plurality of input parameters determined in advance based on a scale different from the image processing parameter is received, and the input parameter is converted into the image processing parameter. By doing so, even when the details of the image processing contents are not known, it is possible to perform image processing with appropriate processing parameters with a simple operation.

  (2) Rather than directly inputting image processing parameters, as one or a plurality of input parameters determined in advance based on a scale different from the image processing parameters, the imaging region, imaging position, irradiation field shape, irradiation field Of the width, the importance of the center of the image, the importance of the specific area in the image, the importance of the high frequency component in the image, the importance of the granularity in the area in the image, the difference from the specified value of the average density in the area in the image An input of one or a plurality of parameters including at least one is received, and the input parameters are converted into image processing parameters. As a result, even if the details of the image processing contents are not known, it is possible to perform image processing with appropriate processing parameters by a simple input parameter input operation that is easy to grasp.

  (3) As described above, instead of directly inputting the image processing parameter, the input of one or more input parameters determined in advance based on a scale different from the image processing parameter is received, and the contrast adjustment parameter is received from the input parameter. This is a case where the details of the image processing contents are not known by converting to one or a plurality of image processing parameters including at least one of tone processing parameters, frequency enhancement processing parameters, and equalization processing parameters. However, it is possible to perform image processing with appropriate processing parameters with a simple operation.

  (4) In the parameter conversion from the input parameter to the image processing parameter, since the conversion is executed based on the decision making theory, a combination at the time of conversion from a plurality of input parameters or conversion to a plurality of image processing parameters is performed. Conversion in consideration is possible.

  (5) In parameter conversion using decision-making theory from input parameters to image processing parameters, conversion is executed based on fuzzy integration. Therefore, conversion from a plurality of input parameters or conversion to a plurality of image processing parameters is performed. Conversion in consideration of the combination at the time is possible.

The best mode for carrying out the present invention will be described below in detail with reference to the drawings.
BEST MODE FOR CARRYING OUT THE INVENTION Preferred embodiments of an image processing method, an image processing apparatus, and an image processing program of the best mode for carrying out the present invention will be described. Note that the present invention is not limited thereby.

Note that each means of the embodiment of the present embodiment can be configured by hardware, firmware, or software.
For this reason, FIG. 1 is shown as a functional block diagram in accordance with the processing procedure of each step of the image processing method, each means of the image processing apparatus, and each routine of the image processing program.

  Hereinafter, the configuration and operation of the best mode for carrying out the present invention will be described in detail with reference to the block diagram of FIG. 1, the flowchart of FIG. 2, and other explanatory diagrams. Each unit in FIG. 1 represents not only each unit of the image forming apparatus but also each step of the image processing method and each routine of the image processing program.

<Overall configuration and processing flow>
(A) Overall configuration:
The radiation generating device 30, the radiation image reader 40, and the image processing device 100 are configured as shown in FIG.

  The image processing apparatus 100 includes a control unit 101 that controls each unit, an operation unit 102 that receives operation input, an image data generation unit 110 that generates image data, a weighting unit 120 that performs weighting based on image data, and a plurality of units. A weighting integration unit 130 that integrates the weights of the image data, a parameter conversion unit 140 that converts the input parameters to the image processing parameters, an image processing unit 160 that executes image processing based on the image processing parameters, A display unit 170 that displays various states is configured as shown in FIG. FIG. 1 shows an example in which a plurality of N weighting units 121 to 12N are provided in the weighting unit 120.

  The operation input unit 102 constitutes a parameter input unit that receives input of one or a plurality of input parameters determined in advance based on a scale different from an image processing parameter directly used for image processing.

  The parameter conversion unit 140 converts the input parameters input by the operation input unit 102 into one or a plurality of image processing parameters used by the image processing unit 160 for image processing based on a predetermined relationship. It constitutes conversion means.

(B) Process flow:
The control unit 101 controls various processes associated with radiographic image capturing / reading, weighting, weighting integration, input parameter reception, parameter conversion from input parameters to image processing parameters, and image processing based on image processing parameters. I do.
The control unit 101 receives various operations and settings by the operator via the operation unit 102. At this stage, various operations and settings related to imaging (radiation generation) are accepted.
The radiation from the radiation generator 30 passes through the subject 5, and the radiation that has passed through the subject 5 is read by the radiation image reader 40. The radiation generation by the radiation generator 30 and the reading by the radiation image reader 40 are executed based on the control from the control unit 101.
The signal read by the radiation image reader 40 is converted into image data by the image data generation unit 110 (S1 in FIG. 2).
The weighting unit 120 assigns a weight to each pixel based on a predetermined agreement of the radiation image data (S2 in FIG. 2). If the weight to be given is single (Y in FIG. 2), one kind of weight is generated and given (S3 in FIG. 2).
If there are a plurality of weights to be given (N in FIG. 2 S2), the weighting integration unit 130 integrates the plurality of weights according to a predetermined agreement (S5 in FIG. 2).
The control unit 101 accepts input of one or a plurality of input parameters determined in advance based on a scale different from the image processing parameter used directly for image processing via the operation input unit 102 (S6 in FIG. 2). .
The parameter conversion unit 140 refers to the weight from the weighting unit 120 and uses one or more input parameters input via the operation input unit 102 for image processing based on a predetermined relationship. Conversion into one or a plurality of image processing parameters (S7 in FIG. 2).
The image processing unit 160 performs image processing on the image data from the image data generation unit 110 according to the image processing parameters converted by the parameter conversion unit 140 (S8 in FIG. 2).
If it is necessary to display an image (Y in FIG. 2 S9), the given weight and the converted image processing parameter are superimposed on the image-processed radiation image and displayed (S10 in FIG. 2). If necessary, the input parameter that is the source of generating the image processing parameter is displayed superimposed on the image-processed radiation image.
After the above processes are completed, the processed image data is output to the outside based on the control of the control unit 101 (S11 in FIG. 2).

<Details of each means and step>
(1) Operation and control of each part:
In the control unit 101, first, information such as an imaging part, an imaging posture or an imaging direction is acquired from the operation input unit 102 which is a user interface. Such information is performed by the user specifying an imaging region, an imaging position, and the like. For example, it is input from a user interface of the image processing apparatus having both the display unit 170 and the operation input unit 102 such as a touch panel by pressing a button displaying an imaging region and an imaging position. In addition, a magnetic card, bar code, HIS (hospital information system: network-based information management), etc. are used.

(2) Radiation image input:
The radiation generator 30 is controlled by the control unit 101, and the radiation emitted from the radiation generator 30 is applied to the imaging panel mounted on the front surface of the radiation image reader 40 through the subject 5. The radiation image reader 40 detects the radiation transmitted through the subject 5 and acquires it as an image signal.

  Specific examples of the configuration include those described in JP-A-11-142998 and JP-A-2002-156716 as using a stimulable phosphor plate. For those using a flat panel detector (FPD) as an input device, those described in Japanese Patent Laid-Open No. 6-342098 that directly convert detected X-rays into charges and obtain them as image signals, There is an indirect system described in Japanese Patent Laid-Open No. 9-90048 in which detected X-rays are once converted into light and then received and converted into electric charges.

  The radiation image reader 40 irradiates the silver salt film on which the radiation image is recorded with light from a light source such as a laser or a fluorescent lamp, and photoelectrically converts the transmitted light of the silver salt film to generate image data. Also good. Moreover, the structure which converts radiation energy directly into an electrical signal using a radiation quantum counting type detector, and produces | generates image data may be sufficient.

  When obtaining a radiation image of the subject 5, the subject 5 is assumed to be positioned between the radiation generator 30 and the imaging panel of the radiation image reader 40, and the radiation emitted from the radiation generator 30 is applied to the subject 5. Irradiation and radiation transmitted through the subject 5 are incident on the imaging panel.

(3) Region of interest setting:
By the way, when taking a radiographic image, for example, in order to prevent radiation from being applied to a part that is not required for diagnosis, or to a part that is not required for diagnosis, radiation scattered in this part is used for diagnosis. In order to prevent the resolution from being reduced by being incident on a required portion, a radiation non-transparent material such as a lead plate is installed in a part of the subject 5 or the radiation generator 30 so that the radiation field to the subject 5 is irradiated. Irradiation field restriction is performed to limit the above.

  When this irradiation field stop is performed, if level conversion processing and subsequent gradation processing are performed using the image data of the irradiation field area and the irradiation field area, the image data in the irradiation field is determined by the image data of the irradiation field area. Image processing of a part required for diagnosis is not performed properly. For this reason, the image data generation unit 110 performs irradiation field recognition for discriminating between the irradiation field area and the irradiation field area.

  In the irradiation field recognition, for example, a method disclosed in Japanese Patent Laid-Open No. 63-259538 is used, and an image on a line segment from a predetermined position P on the imaging surface toward the end of the imaging surface as shown in FIG. 3A. For example, differential processing is performed using the data. As shown in FIG. 3B, the differential signal Sd obtained by this differentiation process has a signal level that is large at the irradiation field edge portion. Therefore, one signal field edge candidate point EP1 is obtained by determining the signal level of the differential signal Sd. It is done. A plurality of irradiation field edge candidate points EP1 to EPk are obtained by performing the process of obtaining the irradiation field edge candidate points radially about a predetermined position on the imaging surface. An irradiation field edge portion is obtained by connecting adjacent edge candidate points of the plurality of irradiation field edge candidate points EP1 to EPk obtained in this way with straight lines or curves.

  Moreover, the method shown by Unexamined-Japanese-Patent No. 5-7579 can also be used. In this method, when the imaging surface is divided into a plurality of small areas, the radiation dose of the radiation is reduced substantially uniformly in the small areas outside the irradiation field where radiation irradiation is blocked by the irradiation field stop. Becomes smaller. In addition, in a small region within the irradiation field, the radiation value is modulated by the subject, so that the dispersion value is higher than that outside the irradiation field. Further, in a small region including the irradiation field edge portion, the portion with the smallest radiation dose and the portion with the radiation dose modulated by the subject coexist, so the dispersion value is the highest. From this, the small area including the irradiation field edge portion is determined by the dispersion value.

  Moreover, the method shown by Unexamined-Japanese-Patent No. 7-181409 can also be used. In this method, image data is rotated about a predetermined center of rotation, and rotation is performed until the boundary line of the irradiation field becomes parallel to the coordinate axis of the orthogonal coordinates set on the image by the parallel state detection means. When the state is detected, the linear equation of the boundary before rotation is calculated by the linear equation calculation means based on the rotation angle and the distance from the rotation center to the boundary line. Thereafter, by determining a region surrounded by a plurality of boundary lines from a linear equation, the region of the irradiation field can be determined. When the irradiation field edge portion is a curve, for example, one boundary point is extracted based on the image data by the boundary point extraction means, and the next boundary point is extracted from the boundary candidate point group around this boundary point. Similarly, by sequentially extracting boundary points from the boundary candidate point group around the boundary points, it is possible to determine whether the irradiation field edge portion is a curve.

  When the irradiation field recognition is performed, an area for determining the level distribution of the image data DT from the radiation image reader when the distribution of the image data DT from the radiation image reader is converted into a distribution at a desired level. (Hereinafter referred to as “region of interest”).

In the setting of the region of interest, for example, the entire lung field is set so that all regions important for diagnosis are included in the chest front image.
In this embodiment, irradiation field recognition and region of interest setting are not necessarily required. In this case, in addition to the weight candidate calculation performed below, by giving a low weight to a portion where the variance value of the pixel value in the fixed region is low, the region that is not exposed and the region outside the irradiation field are lowered and the human body region is raised A method of considering weighted weight candidates may be used. In this case, it is desirable to take an area of about 1/40 to 1/20 of the image as the fixed field for obtaining the dispersion value as an irradiation field area or a blank area.
(4) Weighting:
Here, the weighting unit 120 assigns a weight to each region divided into pixels or a predetermined size in the image based on a predetermined arrangement of the radiographic image.

(4a) Weighting a predetermined position of the image area:
As the weighting of the image data, for example, a method of giving a weight in advance with respect to a predetermined position of the image can be considered. In this method, a template or the like is prepared for each imaging region or imaging position, and a predetermined weight is given to a predetermined position in the image according to the importance of diagnosis. For example, in the chest front image, a method of giving importance 1 to the area A shown in FIG.

(4b) Weighting edge points of image area:
Further, by extracting an edge of an image using a Laplacian filter or a differential filter and using the contrast of the edge, it is possible to give a high weight to the edge of the portion constituting the subject structure which is important for diagnosis. In addition to the above-described filter, multi-resolution such as a wavelet or a Gauss-Laplacian filter can be used for the edge detection. Regarding this part, “Basic Theory of Wavelet Analysis”, Kohei Arai, Morikita Publishing Co., Ltd., p80, 2000 publication can be referred to.

  That is, as the weight, for example, the absolute value of the edge component obtained from the image using the filter can be taken. In addition, by using the functions shown in FIGS. 5 (a) and 5 (b), the importance of large edges caused by artifacts such as small portions such as noise and irradiation field edges is reduced. can do.

  Here, α and β in FIG. 5 are desirably set to about 10% and 90% of the edge maximum value signal value, respectively. 6A shows the original image of the cervical spine, and FIG. 6B shows the detected edges of the cervical spine image of FIG. 6A.

  Furthermore, for edge extraction, it is possible to detect edges more efficiently by using a three-channel filter bank. The three-channel filter bank is a filter processing means for performing filter processing on digital data, and is configured in the form of a three-channel filter bank, and is a decomposition filter for decomposing digital data by a plurality of filters having different characteristics. And a downsampling unit that downsamples each of the decomposition outputs of the decomposition filter unit, and a reconstruction filter unit that receives the output of the downsampling unit and reconstructs the decomposed digital data. In other words, the decomposition filter unit indicates a Laplacian filter, a difference (Sobel) filter, and a simple average filter.

(4c) Image center importance and image edge weighting:
Since a portion important for diagnosis is often set at the center of the image, a method of giving higher weight to the center of the image as shown in FIG. 7 is also effective. Conversely, a higher weight can be given toward the end. In this way, it is possible to give a weight suitable for shooting in which the subject is often located at the edge of the image. As such a part, for example, mammography or pantomography can be considered.

(4d) Weighting to the field edge:
In addition, when the density of the image is remarkably high or low, a low weight can be checked, and a connection degree with a neighboring edge can be checked, and a weight can be given according to the connection degree. Specifically, the edge detection image obtained as an edge with a value greater than or equal to a threshold value such as a Laplacian filter is converted into a parameter space using Hough transform, and a straight line or circle with one or more votes in this parameter space is reversed. Guided by the Hough transform, the weight of the pixel on the straight line or circle is calculated by a graph as shown in FIG. 8A according to the number of votes. By doing so, it is possible to reduce the weight of an unnecessary point such as an irradiation field edge and to give a high weight to other regions. Here, the value of α in FIG. 8 (a) varies depending on the length of the edge to be detected, and it is desirable to change it depending on the imaging region and imaging position, but empirically, it is about 1/3 of the vertical or horizontal width of the image. Size is desirable. For Hough transform, refer to "Basics of Image Recognition [II]-Feature Extraction, Edge Detection, Texture Analysis", written by Shunji Mori and Reiko Itakura, Ohmsha, 1990 publication, etc.

(4e) Weighting based on statistics (such as variance):
Furthermore, binarization processing is performed from the original image (Fig. 9 (a)) using discriminant analysis method to recognize the missing region and the irradiation field region in the image (Fig. 9 (b)), and the average of these regions. It is also possible to examine the dispersion value and weight according to the average dispersion value. In this case, by applying a coefficient calculated by a graph such as that shown in FIG. 10 to the weight of the entire image, the heavier overall weight can be increased for an image having a better granularity. Although the variance value is used here, other statistics can be used. In this case, for example, there is a method of creating a histogram relating to pixel values and giving weights according to this frequency distribution. By doing in this way, it is possible to give a high weight to a pixel value having a high appearance frequency.

  Furthermore, on the assumption that the weight obtained by these methods is a weight candidate and weight integration described later is executed, any of a plurality of weights can be combined and executed in parallel.

  In the above description, the method of determining the weight in units of pixels has been described. However, these methods may be determined for a region divided into a predetermined size of the image as a matter of course. In this case, for example, after thinning out the image at a predetermined ratio, weighting the thinned image in units of pixels as described above, and then reflecting the weight of the thinned pixel corresponding to the original image or an average value of the predetermined region Can be considered as the pixel value of the region, and weighting as described above can be performed.

(5) Weighted integration:
Here, the weighting integration unit 130 integrates a plurality of weights calculated by the weighting unit 120 so that combinations more important for diagnosis are regarded as important. Thereby, it integrates so that the area | region required for a diagnosis may be regarded as important. As a result, it is possible to perform processing focusing on the area necessary for diagnosis.

  That is, the weighting integration unit 130 determines a final weighting by using the weighting candidates for the plurality of weighting candidates. Note that the final weight is normalized such that the obtained weight is, for example, the maximum value is 1, and the maximum value is used by using the maximum or minimum value of the weight by each method. The weight that was most effective for can be selected, and if the minimum value is used, it is possible to give the most careful weight for selecting the degree of importance guaranteed at the minimum.

In addition, by using the fuzzy integral used in the following decision making theory, it is possible to give a weight considering the combination of each method.
For this fuzzy integration, for example, the Choquet integration can be used as follows. To use this method, it is necessary to give the fuzzy measure necessary for fuzzy integration. The fuzzy measure is a measure space (X, F, μ) in which the condition of complete additivity is relaxed from the condition of the measure required by Lebesgue integration. Specifically, the following conditions are usually required.

When X is a set and F = 2X, μ is given as follows.
1.μ (φ) = 0,
2. μ (X) = 1,
3. When A∈2X, 0 ≦ μ (A) <∞,
4. When A, B∈2X, if A⊆B⊆X, μ (A) ≦ μ (B),
For example, if the weighting candidates include “edge strength”, “image medianity”, and “image density”, these sets can be expressed as a power set 2X if X = {edge strength, image medianness, image density}. On the other hand, it means that a measure that takes into account a subjective scale is given as follows.

φ = 0.0,
{Edge strength} = 0.6,
{Image centrality} = 0.3,
{Edge strength, image centrality} = 0.8,
{Image density} = 0.3,
{Image density, edge strength} = 0.7,
{Image density, image median} = 0.9,
{Image density, image medianness, image edge strength} = 1.0,
The Choquet integral is defined as follows for the measure thus determined.

In the above equation, each element of X is x_ {1}, x_ {2}, ... in order of increasing weight (measure), and A_ {1} is an element corresponding to X to x_ {1} If A_ {2} is a set excluding the elements corresponding to x_ {1}, x_ {2}, ...
(C) ∫hdμ = Σ (x_ {I} -x_ {i-1}) μ (A_ {I}) (a_ {0} = 0),
It is to calculate with.

For example, if the candidate weights of the target pixel are edge strength = 0.6, image medianity = 0.5, and image density = 0.7, respectively, the fuzzy integration result is
1.0 * 0.5 + 0.8 * (0.6-0.5) + 0.5 * (0.7-0.6) = 0.63,
This means that the area of FIG. 11 is obtained. As described above, the fuzzy integral corresponds to the area of the overlapping according to the weight based on the scale using the subjective scale, and enables the integration of the weight reflecting the subjective scale. In this regard, you can refer to “Fuzzy Theory”, Hiroshi Inoue and Michio Amagasa, Asakura Shoten, P.89-104, 1997.

  It is also possible to execute a plurality of types of weighting and select any one of the weighting candidates obtained by the plurality of weights by an operation from the operation unit 102. In addition, other fuzzy integrals such as Sugano integral can also be used.

(6) Parameter input:
Here, the input parameter is one or a plurality of parameters determined in advance to be input via the operation input unit 102 based on a scale different from the image processing parameter used for image processing.

  Specifically, imaging location, imaging position, irradiation field shape, irradiation field width, importance in the center of the image, importance in the specific area in the image, importance in the high-frequency component in the image, granularity in the area in the image Are one or a plurality of parameters including at least one of the importance level and the difference from the specified value of the average density in the image area.

  That is, even when the operator does not know the details of the image processing contents, simple input parameters that are easy for the operator to grasp are determined in advance together with correspondence relationships that can be converted into image processing parameters described later. For this reason, it is good also considering the parameter defined based on the subjective scale as an input parameter.

  It should be noted that parameters (image processing parameters) for executing image processing generally include coefficient of function, weight of each element, etc., and the index is not always easy for the operator to understand intuitively. Absent.

  Therefore, when adjusting the image processing, if the meaning of the parameter is not understood, appropriate adjustment cannot be performed. However, this problem can be avoided by preparing parameters (input parameters) that can be intuitively understood as follows and having procedures for converting them into image processing parameters.

  It should be noted that it is desirable that the parameters are normalized so as to have values from 0 to 9 as much as possible. In this way, the adjuster can clearly determine how much the set value has entered the allowable range of the parameter.

(6a) Irradiation field shape:
Based on a scale different from the image processing parameters in advance for the input field parameters such as “1. Circular”, “2. Square”, “3. Hexagon”, “4. Other polygons”, etc. And set as a predetermined parameter.

(6b) Area of irradiation field:
The input parameter is a numerical value normalized as “0” to “9”, and the irradiation field width is set in advance as a parameter determined in advance based on a scale different from the image processing parameter.

(6c) Image center importance:
The input parameter is a numerical value normalized as “0” to “9”, and the image center importance is set in advance as a predetermined parameter based on a scale different from the image processing parameter.

(6d) Specific area importance:
The input parameters are set as “1. Image right edge side”, “2. Image left edge side”, “3. Image upper edge side”, “4. Image lower edge side”, “5. Other area”, etc. The importance is set in advance as a parameter determined in advance based on a scale different from the image processing parameter so as to increase the importance on the designated direction side of the image according to the input value.

(6e) Importance of high-frequency components in images:
Based on a scale different from the image processing parameter in advance so that the input parameter is a numerical value normalized from “0” to “9” and the like. It is set as a predetermined parameter.

(6f) Image granularity suppression importance:
The input parameter is a numerical value normalized from “0” to “9”, etc., and is determined in advance based on a scale different from the image processing parameter so that the higher the numerical value, the lower the enhancement degree of the frequency processing. Set as a specified parameter.

(6g) Importance of difference between image density and specified density:
The input parameter is a numerical value normalized from “0” to “9”, etc., and the larger the numerical value is, the higher the frequency of appearance of the image histogram in the region is. In order to make it narrower, it is set in advance as a parameter determined in advance based on a scale different from the image processing parameter.

(6h) Site information:
The input parameter is a numerical value normalized as “0” to “9” and the like, and based on a scale different from the image processing parameter in advance so that the larger the numerical value, the larger the size of the part. It is set as a predetermined parameter. Specifically, the above parameters may be set to about 0 to 3 for a finger or the like, and about 8 to 9 for an abdomen or the like.

(7) Parameter conversion:
Here, the image processing parameter is a parameter used for image processing in the image processing unit 160. In the present embodiment, the image processing parameter is a parameter different from the input parameter input from the operation input unit 102. It is predetermined that the processing parameter is converted.

  Here, the image processing parameter is one or more parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter.

Note that parameters (image processing parameters) for executing image processing generally include a coefficient of a function, a weight of each element, and the like.
In the parameter conversion from the input parameter to the image processing parameter in the parameter conversion unit 140, it is desirable that the conversion is executed based on the decision making theory. Here, by providing conversion means based on the decision making theory in the parameter conversion unit 140, conversion from a plurality of input parameters or conversion considering a combination at the time of conversion to a plurality of image processing parameters becomes possible. .

  In the parameter conversion using the decision making theory from the input parameter to the image processing parameter, it is more preferable that the conversion is executed based on fuzzy integration. By using fuzzy integration in this way, it is possible to convert from a plurality of input parameters, or to consider a combination when converting to a plurality of image processing parameters.

  And by performing such parameter conversion, even if you do not know the details of the image processing content, only a simple input operation based on the subjective scale matched the actual image processing Image processing can be performed with appropriate processing parameters.

Each image processing parameter to be converted according to the size of the input parameter may be converted linearly, for example, or may be a non-linear conversion table as shown in FIG.
Specific example of parameter conversion:
The following specific example can be considered as the parameter conversion from the input parameter to the image processing parameter in the parameter conversion unit 140.
・ Irradiation field shape → Processing parameters for edge extraction,
・ Irradiation field size → Processing parameters for edge extraction,
-Image center importance → Processing parameter to obtain the variance value when weighting the image center,
-Image specific area importance → Processing parameter to obtain the variance value when weighting a specific area,
・ Image high frequency component importance → gradation processing parameters,
Image granularity suppression importance → frequency enhancement processing parameters,
The importance of the difference between the image density and the specified density → Contrast adjustment parameter to prevent the density difference from becoming large,
-Body part information (large to small) → weight parameter to image area (whole image to center of image),

(7a) Irradiation field shape:
If an irradiation field shape (“1. Circular”, “2. Square”, “3. Hexagon”, “4. Other polygons”, etc.) is input as an input parameter, the set value will be The shape of the figure detected by the Hough transform is changed. Specifically, for the extraction of figures by Hough transform, for example, in straight line extraction (first image processing technology, written by Akio Okazaki, Industrial Research Committee, P. 100) A group of straight lines passing through y) can be expressed by ρ = xcos θ + ysin θ. When one of ρ and θ is fixed, one straight line on the image is determined. Therefore, when a group of straight lines passing through the edge points extracted by the above edge extraction or the like is plotted in the (ρ, θ) space, if a plurality of edge points are located on the same straight line, (ρ, θ) A plurality of curves in the space intersect at one point (see FIG. 13).

  A straight line can be detected by selecting a straight line in which the number of intersecting curves (number of votes) at the intersection is equal to or greater than a threshold value. Similarly, a group of circles centered at (c1, c2) and having a radius of (x1, x2) and (c1, c2) is a group of circles passing through the point (x1, x2). If the same processing is performed using (c1, c2) as a parameter space, the edge of the circle can be detected. If the shape of the edge to be detected in advance is known in this way, not only the processing time is shortened by performing only one process, but a large arc close to a straight line is not erroneously recognized as a straight line. Extraction accuracy can be increased. Then, by inputting this shape, the operator can extract edges with high accuracy without being aware of the mechanism such as the internal Hough transform.

(7b) Irradiation field size:
When an irradiation field size (such as “0. very narrow” to “9. extremely wide”) is input as an input parameter, by increasing the threshold value of the number of votes in the Hough transform as the input value increases, The edge of the irradiation field can be extracted more accurately. In this case as well, the operator is not conscious of the image processing operation or image processing algorithm inside the apparatus, and may input an intuitive value (input parameter) of the irradiation field width.

(7c) Image center importance:
When the image center importance (a numerical value normalized as “0” to “9”, etc., the importance for the image center) is input as an input parameter, the smaller the value, the more uniform weighting and the larger the input value. The dispersion value of the calculation formula when weighting the center more greatly is obtained. By doing so, parameters are converted so that the smaller the value, the more important the entire image is, and the larger the value, the more important the image center is.

(7d) Specific area importance:
Specific area importance as an input parameter (“1. Image right edge side”, “2. Image left edge side”, “3. Image upper edge side”, “4. Image lower edge side”, “5. Other areas”, etc.) Is input, the parameter is converted so as to obtain the variance value of the calculation formula when the weight is greatly weighted at the position corresponding to the input parameter.

(7e) Importance of high-frequency components in images:
If the importance of a high frequency component (a numerical value normalized as “0” to “9”, etc., where the higher the numerical value is, the more important the frequency characteristic of the frequency processing is, the higher frequency is important) is input as an input parameter. The larger the is, the more the frequency characteristics of the frequency processing are converted into parameters for frequency enhancement processing and gradation processing so that high frequency is important.

  When using a three-channel filter as a filter bank, by selecting a combination of fuzzy measures with a high measure of the edge area obtained from the high resolution level, specifically, the top and second ones of the decomposition level, An operator can set gradation processing parameters optimized for high-frequency components without being conscious of internal processing.

  In addition, when the size of the mask used for the filter processing is M, the input parameter p is typed and the conversion formula M = 30−3 × p for parameter conversion is used. High frequency components can be emphasized.

(7f) Image granularity suppression importance:
When an image granularity suppression importance (a numerical value normalized as “0” to “9”) is input as an input parameter, the larger the numerical value, the lower the frequency processing enhancement level and the lower the granularity. It is converted into an image processing parameter for frequency processing.

  Specifically, when the frequency processing correction value is FH, FH = p / 10 * 2 is calculated for the input parameter p. When the γ correction value is GH, GH = p / 10 * 3 is calculated. By using these FH and GH to multiply the frequency emphasis correction value and the average output γ value, it is possible to perform processing with suppressed granularity.

(7g) Importance of difference between image density and specified density:
When the importance of the difference between the image density and the specified density (a numerical value normalized as “0” to “9”) is input as an input parameter, the larger this numerical value, the more frequently the image histogram appears in the area. In order to narrow the limit of the difference between the output density of the signal value and the ideal output density, it is converted into a contrast adjustment parameter that prevents the density difference from becoming large.

  Specifically, for example, when the difference limit value is R, the limit value R is calculated by setting R = p * 0.3 or the like for the input parameter p, and processing is performed so that the density difference does not become larger than this value. By limiting this, it is possible to easily set processing parameters such that the density in the region always has a certain difference from the specified range.

(7h) Site information:
As input parameters, such as “0” to “9” such as “1. smallest part such as finger”, “3. small part such as palm”, “9. largest part such as abdomen”, etc. And a normalized numerical value) are converted into parameters that change the weight of importance according to the size corresponding to the input part. For example, in the case of a small part such as a finger, the image centrality is set so that a higher weight is placed toward the center, and a substantially uniform weight is applied to the entire image in a large part such as the abdomen.

(7i) Conversion when multiple parameters are not independent:
In addition, when the plurality of image processing parameters described above are not independent of each other and optimum parameters can be obtained by adjusting each other to an appropriate value, an appropriate value must be obtained unless the image processing content is well known. It was almost impossible to get. However, in this embodiment, such a problem can be solved.

Here, for example, “image sharpness” and “image contrast” are considered as a plurality of image processing parameters that are not independent of each other.
These values are given as ranks of 0 to 9 (the higher the value, the higher the sharpness and the contrast) as normalized input parameters. At this time, for example, the enhancement degree β of frequency enhancement processing and γ of gradation processing are determined by the following equations.
β = R1 * 0.3 * F,
γ = R2 * 0.5−β * 0.8,
Here, R1 is an input parameter for image sharpness, and R2 is an input parameter for image contrast. F is a correction parameter and is determined by the frequency band to be emphasized, and is determined so as to take a smaller value as the frequency band to be emphasized is lower.

Here, for example, if the size of the mask for frequency emphasis processing using simple average is M,
F = 1.1−M / 3 * 0.1,
Can be given in

By giving in this way, the user is operating as two independent parameters by internally converting mutually related parameters.
For this reason, when a plurality of image processing parameters are not independent of each other, the final appropriate value can be obtained by adjusting the desired input parameters by converting the related parameters internally, even if the user does not know the details of the image processing. It becomes possible to obtain.

(8) Image processing based on image processing parameters:
Here, the image processing on the radiation image using the image processing parameter in the image processing unit 160 corresponds to gradation processing, equalization processing, frequency enhancement processing, contrast adjustment processing, and the like.

(8a) Gradation processing:
The image processing unit 160 performs gradation processing as one of the image processings based on the image processing parameters converted by the parameter conversion unit 140 and the weights integrated by the weighting integration unit 130 for the image data from the image data generation unit 110. Is executed.

  In this gradation processing, gradation processing conditions are determined using the following evaluation function. Here, the feature amount evaluation function is set as follows using the LUT shift value S and the rotation amount G as parameters.

Here, f (x) represents a function for correcting the signal amplification factor, and W (i, j) represents the weight of xij.
Specifically, f (x) is, for example,
f (x) = x-1, where x ≧ 1.
f (x) = − (1 / x−1), where 1>x> −1.
f (x) =-C, where x = 0.
It is.

  Here, C is an amplification factor when the signal is converted to zero. By passing through such a correction function, it is possible to evaluate the gains with high or low gains by adding or subtracting points. The s, g value is determined as a value that maximizes EI (s, g).

  By doing so, it is possible to convert the pixels having no image and having a high weight so as to be most effectively amplified by the LUT. It should be noted that Δ in each of the above formulas is preferably about 3 to 5.

(8b) Equalization processing:
The equalization process is a process that makes it possible to keep all areas in an image within the visible range by compressing the dynamic range of the image. On the other hand, if this process is applied strongly, the contrast of the entire image is lost. It is desirable to perform appropriate compression.

To achieve this dynamic range compression,
First, a weighted histogram H (x) is generated. Here, x is a variable that takes the range of the dynamic range of the image as a value. For a 12-bit image, 4095 ≧ x ≧ 0. This H (x) is

Defined by However, here, Σ scans the entire image and takes the sum only when the pixel value is x.
Here, D (x) is a weighted histogram correction function. For example, by making it as shown in FIG. 14, the overall equalization processing can be adjusted to increase the weight of the high signal value. This is effective when emphasizing the depiction of the skin.

Considering such H (x), it is possible to perform evaluation in consideration of both the weight and the number of pixels.
Next, this H (x) is evaluated. The evaluation is performed only for the pixel value having this value, assuming that the pixel in which the value of H (x) is larger than the predetermined threshold value is a pixel value containing a lot of important information.

  For the pixel value x exceeding the threshold value, the amplification factor is calculated by A (s, g) (x) with respect to the LUT determined as the gradation processing condition. If there is a pixel in which A (s, g) (xij) is smaller than a predetermined threshold, the parameter for determining the degree of equalization processing is changed in a direction to increase and the same evaluation is performed again on the equalization processing image. I do. By repeating this operation until the pixel value equal to or less than the threshold value disappears or until the condition value of a predetermined parameter is set, an appropriate equalization process can be performed.

(8c) Frequency enhancement processing:
The frequency enhancement process is performed by enhancing the high frequency component of the image, and the sharpness of the image can be improved. However, when processing is performed more than necessary, there is a problem of deteriorating the graininess of the image. This frequency enhancement process is performed as follows using a weighted image. That is, the enhancement correction coefficient calculated from the graph of FIG. 15 is applied to a coefficient representing the enhancement degree of frequency processing.

  As a result, it is possible to reduce the enhancement degree of the frequency processing with a pixel having a small weight for each pixel, and it is possible to selectively give a weak enhancement degree to a region unnecessary for diagnosis, such as noise or an irradiation field.

  Further, this enhancement correction coefficient may be attenuated by taking a negative value at a low importance pixel as shown in FIG. Furthermore, processing that reflects the granularity of the image is possible by using image granularity when creating this weighted image and using a method such as setting a high granularity weight or increasing the measure by fuzzy integration. Yes, the overall processing can be suppressed for images with poor graininess.

(8d) Contrast adjustment processing:
As the image contrast adjustment, for example, there is a method of referring to the image centrality and, when the importance is small, increasing the frequency enhancement degree to lower the gradation processing contrast. The relationship between the degree of enhancement and γ at this time depends on the frequency band to be emphasized and is not applicable to all cases. However, the degree of enhancement of frequency processing (high-frequency component) is reduced against γ by 0.1. It is preferable to increase the degree of addition) by 0.1.

(9) Display and output:
If it is necessary to display an image of the processed image data on the image display unit 160 (Y in FIG. 2 S9), when displaying the image processed radiographic image, a given weight, input parameters, parameter conversion Any one of the image processing parameters converted by the above is displayed in a state of being superimposed on the radiation image (S10 in FIG. 2). Here, it is desirable to display an image with at least an image processing parameter superimposed on the display of the radiation image.

  By doing so, it becomes clear what weight is given and image processing is performed. Further, by doing so, it becomes clear what image processing parameter converted from the input parameter is given and the image processing is performed. Further, by doing so, the relationship between the input parameter and the image processing parameter is clarified, and it becomes clear that an appropriate input parameter has been input.

  Further, a plurality of types of weighting are executed in parallel, and image processing is executed in accordance with each of the plurality of image processing parameters converted based on the input parameters with reference to the plurality of weights. It is also possible to sequentially display the radiographic images executed in each of the above.

  Also, the correspondence between weights, input parameters, and image processing parameters is displayed together with the image processed radiographic images. If there are a plurality of correspondence and image processed radiographic images, the image processed radiographic image is displayed. , Weights, input parameters, and image processing parameters may be sequentially displayed.

Then, after the above processes are completed, the processed image data is output to the outside based on the control of the control unit 101 (S11 in FIG. 2).
As described above, instead of directly inputting image processing parameters, input of one or a plurality of input parameters determined in advance based on a scale different from the image processing parameters is received, and the input parameters are converted into image processing parameters. By doing so, even when the details of the image processing contents are not known, it is possible to perform image processing with appropriate processing parameters with a simple operation.

1 is a block diagram functionally showing the overall configuration of the best mode for carrying out the present invention. It is a flowchart which shows the flow of the whole process of the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention. It is explanatory drawing which shows the mode of the process in the best form for implementing this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 5 Subject 30 Radiation generating apparatus 40 Radiation image reader 100 Image processing apparatus 101 Control part 102 Operation part 110 Image data generation part 120 Weighting part 130 Weighting integration part 140 Parameter conversion part 160 Image processing part 170 Image display part

Claims (15)

An image processing method for performing image processing on image data of a radiographic image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters,
A parameter input step for receiving input of one or more input parameters determined in advance based on a scale different from the image processing parameter;
A parameter conversion step of converting the input parameter input in the parameter input step into one or a plurality of image processing parameters used for image processing based on a predetermined relationship;
An image processing step of performing image processing using one or more image processing parameters converted in the parameter conversion step;
An image processing method comprising: The input parameters are the imaging region, imaging posture, irradiation field shape, irradiation field width, importance in the center of the image, importance in the specific area in the image, importance in the high frequency component in the image, granularity in the area in the image 1 or a plurality of parameters including at least one of the importance level and the difference from the specified value of the average density in the image area,
The image processing method according to claim 1. The image processing parameter is one or more parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter.
The image processing method according to claim 1, wherein the image processing method is an image processing method. In the parameter conversion step, conversion from input parameters to image processing parameters is executed based on decision making theory.
The image processing method according to claim 1, wherein: The conversion from the input parameter based on the decision making theory in the parameter conversion step to the image processing parameter is performed based on fuzzy integration.
The image processing method according to claim 4. An image processing apparatus that performs image processing on image data of a radiation image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters,
Parameter input means for receiving input of one or a plurality of input parameters determined in advance based on a scale different from the image processing parameter;
Parameter conversion means for converting the input parameters input by the parameter input means into one or more image processing parameters used for image processing based on a predetermined relationship;
Image processing means for executing image processing using one or a plurality of image processing parameters converted by the parameter conversion means;
An image processing apparatus comprising: The input parameters input by the parameter input means include the imaging region, the imaging position, the shape of the irradiation field, the width of the irradiation field, the importance of the center of the image, the importance of the specific area in the image, and the importance of the high-frequency component in the image. One or more parameters including at least one of a degree, a degree of granularity in the image area, and a difference from a specified value of the average density in the image area,
The image processing apparatus according to claim 6. The image processing parameter converted by the parameter conversion means is one or a plurality of parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter.
The image processing apparatus according to claim 6, wherein the image processing apparatus is an image processing apparatus. In the parameter conversion means, conversion from an input parameter to an image processing parameter is executed based on a decision making theory.
The image processing apparatus according to claim 6, wherein the image processing apparatus is an image processing apparatus. The conversion from the input parameter based on the decision making theory in the parameter conversion means to the image processing parameter is executed based on fuzzy integration.
The image processing apparatus according to claim 9. An image processing program for performing image processing on image data of a radiographic image having a signal corresponding to an irradiation dose of radiation that has passed through a subject, using image processing parameters,
A parameter input routine for accepting input of one or more predetermined input parameters based on a scale different from the image processing parameters;
A parameter conversion routine for converting the input parameters input in the parameter input routine into one or more image processing parameters used for image processing based on a predetermined relationship;
An image processing routine that executes image processing using one or more image processing parameters converted by the parameter conversion routine;
An image processing program comprising: The input parameters are the imaging region, imaging posture, irradiation field shape, irradiation field width, importance in the center of the image, importance in the specific area in the image, importance in the high frequency component in the image, granularity in the area in the image 1 or a plurality of parameters including at least one of the importance level and the difference from the specified value of the average density in the image area,
12. The image processing program according to claim 11, wherein The image processing parameter is one or more parameters including at least one of a contrast adjustment parameter, a gradation processing parameter, a frequency enhancement processing parameter, and an equalization processing parameter.
13. The image processing program according to claim 11, wherein the image processing program is any one of claims 11 and 12. In the parameter conversion routine, conversion from an input parameter to an image processing parameter is executed based on a decision making theory.
14. The image processing program according to claim 11, wherein the image processing program is any one of claims 11 to 13. The conversion from the input parameter based on the decision making theory in the parameter conversion routine to the image processing parameter is performed based on fuzzy integration.
The image processing program according to claim 14.

Images