JPH0950521A - Method and device for processing picture - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently emphasize only a specific picture part in question without emphasizing a component such as a noise component unnecessary for picture reading by suppressing the undershoot and overshoot of a picture. SOLUTION: A high frequency component Ssp in an original picture signal Sorg is obtained by a low pass filter 11 and a computing element 14a, a morphology operation is applied to the component Ssp to obtain a morphology signal Smor indicating a large value on a required picture part and a small value on the other picture part, an emphasis coefficient β (Smor) corresponding to the signal Smor is found out from a conversion table 13, and a processed picture signal Sproc emphasizing only a required picture part is obtained by applying emphasis processing depending on morphology.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method and apparatus, and more particularly to an image processing method and an image processing method for selectively enhancing only a specific image portion such as an abnormal shadow or an image portion having high contrast in an image. It relates to the device.

[0002]

2. Description of the Related Art Conventionally, an image signal representing an image obtained by various image acquisition methods has been subjected to image processing such as gradation processing and frequency processing to improve the observation / interpretation performance of the image. It is being appreciated. Particularly in the field of medical images such as radiographic images of the human body, a specialist such as a doctor needs to accurately diagnose the presence or absence of a disease or injury of a patient based on the obtained image, and the image Image processing that improves the image interpretation performance of is becoming indispensable.

As the so-called frequency enhancement processing in this image processing, for example, as shown in Japanese Patent Laid-Open No. 61-169971, an image signal (called an original image signal) Sorg such as a density value of an original image is

[0004]

(Equation 8)

It is known to convert the image signal Sproc into Here, β is a frequency enhancement coefficient, and Sus is a non-sharp mask (so-called blur mask) signal. This blur mask signal Sus sets a mask composed of a pixel matrix of N columns × N rows (N is an odd number) with the original image signal Sorg as the center pixel for pixels arranged two-dimensionally, that is, a blur mask.

[0006]

[Equation 9]

It is an ultralow spatial frequency component required as

The value in the second term parenthesis of the equation (8) (Sorg-S
us) is obtained by subtracting the blur mask signal, which is an ultra-low spatial frequency component, from the original image signal, so that the frequency component higher than the ultra-low spatial frequency of the original image signal from which the ultra-low spatial frequency component is removed is means. This relatively high frequency component can be emphasized by multiplying the relatively high frequency component by the frequency enhancement coefficient β and then adding the original image signal.

On the other hand, a morphology (Morpology) that selectively extracts only a specific image portion such as an abnormal shadow in the image
A process based on an algorithm of hology (also referred to as morphology or morphology) (hereinafter referred to as morphological operation or morphological process) is known. This morphological processing has been studied as an effective method for detecting a microcalcification image that is a characteristic morphology in breast cancer, but the target image is not limited to such a microcalcification image in a mammogram. If the size or shape of the specific image portion (abnormal shadow or the like) to be detected is known in advance, it can be applied to any image.

An outline of the morphology processing will be described below by an example in which the morphology processing is applied to detection of a microcalcification image in a mammogram.

(Basic operation of morphology) Morphological processing is generally developed as a set theory in an N-dimensional space, but a two-dimensional grayscale image will be described for intuitive understanding.

A point at coordinates (x, y) is represented by a density value f
It is regarded as a space having a height corresponding to (x, y). Here, the density value f (x, y) is a signal having a high brightness and high signal level, such that the higher the brightness, the larger the value, such as the signal for displaying on the CRT.

First, for simplicity, consider a one-dimensional function f (x) corresponding to the cross section. The structuring element g used in the morphological operation is a symmetric function that is symmetric about the origin as shown in the following equation (10).

[0014]

(Equation 10)

It is assumed that the value is 0 in the domain and the domain G is the following equation (11).

[0016]

[Equation 11]

At this time, the basic form of the morphological operation is a very simple operation as shown in equations (12) to (15).

[0018]

(Equation 12)

That is, the dilation process is a process for searching for the maximum value within the range of ± m (value determined according to the structuring element B) centered on the pixel of interest (FIG. 6). On the other hand, the erosion process is a process for searching for a minimum value within a width of ± m centered on the pixel of interest (see FIG. 6B). Therefore, by subtracting the signal (indicated by a broken line in FIGS. 6A and 6B) subjected to the dilation process or the erosion process from the original image signal, the signal value indicated by a diagonal line in the drawing is sharp. The morphology signal Smor that outputs a non-zero value is obtained only for the edge portion of the image that changes rapidly and the image portion that changes in a spatially smaller range than the structuring element.

The opening (or closing) process corresponds to searching for the maximum value (or minimum value) after searching for the minimum value (or maximum value). Ie opening
The (opening) process is performed from the low-luminance side in the density curve f
This corresponds to smoothing (x) and removing a convex density variation portion (a portion having a higher brightness than the surrounding portion) smaller than the mask size 2 m (see FIG. 6C). On the other hand, clos
In the ing (closing) process, the density curve f
This corresponds to smoothing (x) and removing a concave density variation portion (a portion whose brightness is lower than the surrounding portion) smaller than the mask size 2 m (see FIG. 6D). Therefore, those that have undergone the opening process and closing process (Fig. 6
(Indicated by broken lines in (C) and (D)) is subtracted from the original image signal to eliminate zero only in a characteristic image portion that varies in a spatially smaller range than a structuring element, as indicated by the diagonal lines in the drawing. A morphological signal Smor for outputting a value other than is obtained.

When the structuring element g is not symmetrical with respect to the origin, the dilation operation shown in the equation (12) is Mink.
The owski sum and the erosion operation shown in equation (13) are called the Minkowski difference.

Here, in the case where the density value f (x) is a signal of high density and high signal level in which the density is higher, such as a signal for recording on a negative film, the brightness and the density are higher. Therefore, the dilation process for the high-density high-signal level signal is the same as the erosion process for the high-luminance high-signal level (FIG. 6B), and the erosion process for the high-density high-signal level signal is Corresponds to the dilation process at the high brightness and high signal level (FIG. 6A), and the opening process at the high density and high signal level signal is similar to the closing process at the high brightness and high signal level (FIG. 6D). The closing process for a signal having a high density and a high signal level is the same as the opening process for the high brightness and high signal level (see FIG. 6).
(C)).

In this section, the case of an image signal (luminance value) of high luminance and high signal level will be described.

(Application to Calcification Shadow Detection) For the detection of calcification shadows, a difference method of subtracting a smoothed image from an original image can be considered. Since it is difficult to distinguish between calcified shadows and elongated non-calcified shadows (mammary glands, blood vessels, mammary gland supporting tissues, etc.) by a simple smoothing method, Tokyo University of Agriculture and Technology Obata et al. Used multiple structural elements. We have proposed a morphology filter expressed by the following equation (16) based on the opening operation (“Extraction of microcalcification images by morphological filter using multiple structuring elements” IEICE Transactions D-II Vol .J75-D-II No.7 P1170-1176 July 1992).

[0025]

(Equation 13)

Here, Bi (i = 1, 2, ..., M) is, for example, four linear structural elements (M = 4 in this case) shown in FIG. 7 (the total of these four structural elements is a multiple structure). Hereinafter, the element is simply referred to as a structural element including the case of i = 1). If the structuring element Bi is set to be larger than the calcification shadow to be detected, the calcification shadow, which is a finer signal change portion than the structuring element Bi, is removed by the processing by the opening operation. On the other hand, if the elongated non-calcified shadow is longer than the structuring element Bi and its slope matches any of the four structuring elements Bi, the maximum value of the opening process for each structuring element Bi (equation Even if the calculation of the second term of (16) is obtained, it remains as it is. Therefore, by subtracting the smoothed image (image in which only the calcified shadow is removed) thus obtained from the original image f, an image including only a small calcified shadow is obtained. This is the idea of equation (16).

As described above, in the case of a signal of high density and high signal level, the calcification shadow has a lower density value than the surrounding image portion, and the calcification shadow has a concave signal with respect to the surrounding portion. Since it is a changed part, the closing process is applied instead of the opening process, and the formula (17) is applied instead of the formula (16).

[0028]

[Equation 14]

As described above, the morphological processing is (1) effective for extracting the calcification shadow itself (2) not easily affected by complicated background information (3) the extracted calcification shadow is not distorted There are features such as. That is, this method can detect geometrical information such as the size, shape, and density distribution of calcification shadows better than general differential processing.

[0030]

As described above, in order to improve the image interpretation performance, it is indispensable to perform image processing on a target image.
As disclosed in Japanese Patent Application Laid-Open No. 2-1078, only the density-dependent enhancement processing enhances components that obstruct image interpretation, such as radiation noise components in a mammogram, and thus degrades the interpretation performance. I will let you.

Also, Japanese Patent Publication No. 60-192482 and Japanese Patent Laid-Open No. 2-1209
As disclosed in No. 85, Japanese Translation of PCT International Publication No. 3-502975, etc.,
In the enhancement process depending on the variance of the image signal, an image portion having a large change in density is strongly emphasized locally, so that undershoots and overshoots are relatively conspicuous in the vicinity of the portion, and particularly high density is observed for X-ray images. There is a problem that artifacts easily occur on the side.

The present invention has been made in view of the above circumstances, and efficiently emphasizes only a specific image portion of interest without emphasizing unnecessary components such as noise components for image interpretation, thereby generating an artifact. It is an object of the present invention to provide an image processing method and apparatus in which the above are suppressed.

[0033]

According to the image processing method of the present invention, an unsharp mask signal Sus corresponding to a predetermined spatial frequency of an original image signal Sorg representing an image is obtained, and the unsharp mask signal Sus and the original image signal are obtained. A difference signal Ssp with respect to Sorg is subjected to a morphological operation using a structuring element Bi and a scale factor λ to obtain an image portion in which the difference signal spatially fluctuates smaller than the structuring element Bi and / or the difference signal. Of the morphology signal S showing a characteristic output for the image part where the change of
It is characterized in that the difference signal Ssp is subjected to an enhancement process according to the morphology signal Smor so as to obtain the mor and enhance the image portion.

The structural element B constituting the structural element Bi
As the element, for example, square, rectangular, circular, elliptical, linear, or rhombic elements that are vertically and horizontally symmetrical are desirable. The same applies to the following inventions.

As the emphasizing process according to the morphology signal Smor, the emphasizing process according to the following equation (1) can be used.

[0036]

[Equation 1]

The enhancement coefficient β (Smor) preferably has a function shape as shown in FIG. 3, for example.
That is, the function shape as shown in FIG. 3A has a morphology signal value S that is a radiation noise area (grain area).
The output is suppressed to a low value in a region where mor is small, and fixed to the upper limit of β (Smor) in a region where the morphology signal value Smor, which is a region corresponding to a desired image portion such as a calcification shadow, is extremely large. These intermediate regions are set to monotonically increase as the morphology signal value Smor increases. Also, FIG. 3 (B)
The function shape as shown in (3) is a morphology that is an area corresponding to a desired image portion such as a calcification shadow, in which the output is fixed to zero for an area having a small morphology signal value Smor which is a radiation noise area (grain area). In the area where the signal value Smor is large, the upper limit of β (Smor) is fixed, and in the area where the morphology signal value Smor is larger, β (Smor) is suppressed low, and overshoot occurs even when extreme emphasis is performed. It prevents the occurrence of undershoot and undershoot.

As the morphological operation, various ones represented by the following equations (2) to (6) can be applied.

[0039]

[Equation 2]

[0040]

(Equation 3)

[0041]

(Equation 4)

[0042]

(Equation 5)

[0043]

(Equation 6)

[0044]

[Outside 1]

When the equation (6) shown above is used as the morphological operation, it is desirable to use the equation according to the following equation (7) as the enhancement processing according to the morphological signal Smor.

[0046]

(Equation 7)

Here, for example, as shown in FIG. 5, f (Ssp) has its output fixed to a constant value in the range where the difference signal Ssp is equal to or larger than a predetermined magnitude or is equal to or smaller than a predetermined magnitude. It is desirable to have a function shape. This is to suppress the occurrence of overshoot or undershoot even when extreme emphasis is performed.

When the morphological operation represented by the equation (2) or (3) is applied, the morphological signal Smor
As a result, it is possible to extract an image portion in which the difference signal Ssp changes spatially smaller than the structuring element Bi or an edge portion in which the brightness (density) changes abruptly. Can be effectively emphasized.

Further, by applying the morphology operation represented by the equation (4), the morphology signal Smor is obtained.
As a signal of a pixel that constitutes an image portion in which the difference signal Ssp is larger than the surrounding image portion and spatially smaller than the structuring element Bi (for example, a calcification shadow in an image signal of high brightness and high signal level). Can be extracted, and this image part can be effectively enhanced.

Furthermore, by applying the morphological operation represented by the equation (5), the difference signal Ssp as the morphological signal Smor is smaller than that of the surrounding image portion and is spatially smaller than the structuring element Bi. It is possible to extract the signals of the pixels forming the image portion (for example, the calcification shadow in the image signal of high density and high signal level), and the image portion can be effectively enhanced.

Further, by applying the morphological operation represented by the equation (6), as the morphological signal Smor, the image portion in which the difference signal Ssp changes spatially smaller than the structuring element Bi and the brightness (density) are sharply changed. It is possible to extract the signals of the pixels forming the changing edge portion, and it is possible to effectively enhance such an image portion.

The image processing apparatus of the present invention comprises an unsharp mask signal calculation means for obtaining an unsharp mask signal Sus corresponding to a predetermined spatial frequency of an original image signal Sorg representing an image, the unsharp mask signal Sus and the original image. The difference signal Ssp from the signal Sorg is subjected to a morphological operation using the structuring element Bi and the scale factor λ to obtain an image portion in which the difference signal spatially varies smaller than the structuring element Bi and / or the difference. Morphology signal calculating means for obtaining a morphology signal Smor showing a characteristic output for an image portion where the image signal changes rapidly, and the difference signal Ssp for enhancing the image portion.
And an emphasizing means for performing an emphasizing process according to the morphology signal Smor.

As the emphasizing process according to the morphology signal Smor, the emphasizing process according to the above equation (1) can be used.

It is desirable that the enhancement coefficient β (Smor) have a function shape as shown in FIG. 2, for example, as in the image processing method of the present invention.

Further, as the morphology operation, various ones represented by the above equations (2) to (6) can be applied. Then, when the morphological operation of the equation (6) is used, it is desirable to use the enhancement processing according to the above equation (7).

The structural element Bi (i = 1, 2, ...,
M) is a structural element B whose directions in the two-dimensional plane are different from each other.
Means a set of M structural elements B prepared as above, and in the case of M = 1, it means a vertically symmetrical element, and in the present invention, i ≧ 2 structural elements and i =
Including the case of 1, the structural element is referred to as Bi.
The scale factor λ means the number of times the above-described Minkowski sum calculation and Minkowski difference calculation are performed, and the degree of smoothing increases as the number of times increases.

[0057]

According to the image processing method and apparatus of the present invention, a frequency component (usually an ultra-low spatial frequency component) S corresponding to a predetermined spatial frequency is extracted from an original image signal Sorg representing an image.
By subtracting us, only the relatively high frequency component (difference signal) Ssp from which the frequency components below that frequency of the original image signal Sorg are removed is extracted. The extracted relatively high frequency component Ssp also includes so-called radiation noise which is a high frequency component.

With respect to the relatively high frequency component Ssp thus obtained, the structuring element Bi and the scale factor λ
By performing the morphological operation using, the characteristic output is performed for the image portion where the image signal spatially fluctuates smaller than the structuring element Bi and the characteristic image portion such as the edge portion where the signal value changes abruptly. Obtain the morphological signal Smor shown. Here, although the radiation image is also included in the characteristic image portion, the morphology signal Smor corresponding to the radiation noise is very small.
The morphology signal Smor for the characteristic image portion other than the radiation noise shows a value larger than the morphology signal Smor for the radiation noise. Therefore, the radiation noise can be separated from the characteristic image portion by the morphology signal Smor.

Next, the relatively high frequency component Ssp is subjected to an emphasis process according to the morphology signal Smor, so that the characteristic image portion other than the radiation noise is strongly emphasized, and other characteristic images including the radiation noise are emphasized. Almost no enhancement is performed on the image portion, and thus the characteristic image portion can be selectively enhanced.

As described above, according to the image processing method and apparatus of the present invention, it is possible to efficiently emphasize only a specific image portion of interest without emphasizing a noise component and other components unnecessary for image interpretation. it can. Further, it is possible to suppress the occurrence of artifacts that occur in the variance value dependent enhancement process.

Further, in the morphological operation, a process of calculating the maximum value or the minimum value is performed instead of the process of calculating the variance value of the image signal, so that the operation time can be shortened.

As a morphological operation, equation (2)
Alternatively, when the calculation shown in (3) is performed, the following effects are further obtained. That is, for example, as shown in FIG. 8A, a characteristic image portion to be emphasized (indicated as a “signal region” having a downward convex in the drawing) exists in an image portion having a uniform peripheral density. If the original image signal Sorg is subjected to the dilation processing shown in the second term of the equation (2), the dilation signal Sdi obtained by the dilation processing is the "signal area" of the original image. It substantially matches the original image signal Sorg corresponding to the image portion around the. As a result, the difference between the dilation signal Sdi and the original image signal Sorg takes a non-zero value only in the “signal area”, in other words, the morphological signal Smor in the equation (2) has a predetermined value only in the “signal area”. , But the morphological signals for the other peripheral parts show almost zero.

On the other hand, as shown in FIG. 7B, the "signal area" is present in the image portion where the peripheral density has a gentle density gradient.
If present, this original image signal Sorg
When the dilation processing shown in the second term of the equation (2) is applied to, the dilation signal Sdi obtained by the dilation processing is the original image signal corresponding to the image portion around the “signal area” of the original image. Sorg is translated in the direction in which the signal value increases. As a result, the difference between the dilation signal Sdi and the original image signal Sorg takes a non-zero value for the peripheral images other than the “signal area”, and the “signal area” is considerably larger than in the case of FIG. Take a value. Therefore, the morphological signal shown in the equation (2) also takes a large value over each part of the original image. As a result, when the enhancement process according to the morphology signal is performed, there is a possibility that the enhancement will be overemphasized.

However, according to the image method / apparatus of the present invention, instead of directly performing the morphological operation on the original image signal Sorg, the original image signal Sor
Since a morphological operation is performed on the high-frequency component Ssp which is the difference signal between g and the unsharp mask signal Sus, FIG.
With respect to the gentle concentration gradient as shown in (B), the morphology signal value which is the result of the morphology calculation becomes substantially zero, and therefore the above-mentioned excessive emphasis can be prevented. The same applies to the erosion process shown in Expression (3).

The operation of the image processing method / apparatus of the present invention will be described with reference to FIG. 9. Of the original image signal Sorg as shown in FIG. 9A, the high frequency wave corresponding to the size of the non-sharp mask is used. The components are separated, and only the signal components among them are selectively enhanced by the enhancement coefficient β (Smor), and converted into a processed image signal Sproc in which only the signal components are enhanced as shown in FIG. .

10A and 10B, a processed image signal obtained by the conventional emphasizing process (see (A)) and a processed image signal obtained by the emphasizing process of the present invention (see (B)). Indicates the signal difference. According to the image enhancement processing by the image processing method of the present invention, it is possible to selectively and effectively enhance only a desired signal while suppressing overshoot and undershoot.

[0067]

BEST MODE FOR CARRYING OUT THE INVENTION An image processing apparatus for specifically implementing the image processing method of the present invention will be described below with reference to the drawings.

FIG. 1 is a schematic block diagram showing the image processing apparatus of this embodiment. The image processing apparatus shown in the figure is a low-pass filter 11 for obtaining an unsharp mask signal Sus corresponding to an ultra-low spatial frequency of an original image signal Sorg representing an image.
And the original image signal Sorg from the unsharp mask signal S
An arithmetic element 14a for subtracting us, and a high frequency component S of the original image signal Sorg obtained by the arithmetic element 14a.
By performing a morphological operation using the structuring element Bi and the scale factor λ on sp (= Sorg-Sus), an image portion in which the high frequency component Ssp of the original image signal Sorg spatially fluctuates smaller than the structuring element Bi or , A morphological signal Smor showing a characteristic output for a characteristic image portion such as an edge portion where the signal value changes abruptly.
And a conversion table 13 that receives an input of the morphology signal Smor and outputs an enhancement coefficient βm (Smor) corresponding to the morphology signal Smor.
And the enhancement coefficient βm (S
mor), the original image signal Sorg is subjected to arithmetic processing according to the following equation (1) to obtain the processed image signal Sorg.
This is a configuration including arithmetic elements 14b and 14c for obtaining proc.

[0069]

[Equation 1]

Here, the original image signal Sor representing the image
g may be read in advance from a radiation image by a predetermined image reading device and stored in a predetermined storage unit, or may be directly input from the image reading device. Further, the radiation image in the present embodiment is a mammogram, and the low-pass filter 11 sets a blur mask composed of, for example, a pixel matrix of 3 columns × 3 rows with respect to the original image signal Sorg, and the following formula (9) (N =
The blur mask signal Sus obtained according to (set to 3) is output.

[0071]

[Equation 9]

As the blur mask, a simple average of pixel values in the mask is used as shown in the above equation (9), or the mask is determined according to the distance from the central pixel as shown in the matrix of FIG. It is also possible to use the ones in which the weighting of the pixel values in is changed.

The morphological filter 12 is, for example, 5 columns × with respect to the high frequency component Ssp of the original image signal Sorg.
The high-frequency component Ssp of the original image signal Sorg is spatially processed by the structural element B consisting of the pixel matrix of five rows and the scale factor λ according to the dilation processing shown in the following equation (2). An image signal corresponding to an image portion that fluctuates smaller (for example, a microcalcification portion that indicates breast cancer in a mammogram) and a characteristic image portion such as an edge portion where the signal value changes rapidly is input. In the case where a morphological signal Smor having a large value is output, while an image signal corresponding to an image portion in which the value of the high frequency component Ssp is larger than that of the surrounding image portion or spatially fluctuates more than the structuring element Bi is input. Outputs an extremely small value of the morphology signal Smor. The structuring element B is set in advance according to the shape and size of the desired microcalcification portion to be subjected to the emphasis process.

[0074]

[Equation 2]

The conversion table 13, for example, as shown in FIG. 3A, suppresses the output to a low level in a region where the morphology signal value Smor is a radiation noise region,
It is set to increase the output in a region having a large morphology signal value Smor, which is a region corresponding to a desired image portion such as a calcification shadow, and to monotonically increase as the morphology signal value Smor increases (Fig. 1 is a schematic representation).

The arithmetic element 14b is an arithmetic element for multiplying the output of the arithmetic element 14a and the output of the conversion table 13, and the arithmetic element 14c is an arithmetic element for adding the original image signal Sorg and the output of the arithmetic element 14b.

Next, the operation of the image processing apparatus of this embodiment will be described.

The original image signal Sorg is sent to the image processing apparatus.
Is inputted, the low-pass filter 11 first sets a blur mask composed of a pixel matrix of 3 columns × 3 rows for the original image signal Sorg, and the above equation (9) (N = 3) is set.
According to the above calculation, the blur mask signal Sus is output. Since this blur mask signal Sus is a blur mask composed of a pixel matrix of 3 columns × 3 rows, it has a relatively high frequency signal.

The arithmetic element 14a is provided with an original image signal Sor
The blur mask signal Sus which is the output of the low-pass filter 11 is subtracted from g to output only the high frequency component Ssp (Sorg-Sus) of the original image signal Sorg.

With respect to the output Ssp of the arithmetic element 14a, the morphological filter 12 uses the structuring element B consisting of a pixel matrix of 5 columns × 5 rows and the scale factor λ to perform the arithmetic processing according to the above equation (2). Then, the morphology signal Smor corresponding to the characteristic shape of the image portion and the fluctuation of the signal value is output. Here, when the image portion is a microcalcification portion which is an abnormal shadow or a portion where the contrast changes abruptly, the morphology signal Smor having a large value is obtained.
Is output, and the morphology signal Smor having an extremely small value is output in other cases.

The morphology signal Smor output from the morphology filter 12 is input to the conversion table 13, and the conversion table 13 receives the input morphology signal Smor.
The enhancement coefficient βm (Smor) corresponding to the size of mor is output. In this embodiment, a substantially upper limit value is output as the enhancement coefficient βm (Smor) in the microcalcification portion, and an extremely small value is output in other portions as the enhancement coefficient βm.
It is output as (Smor).

The arithmetic element 14b multiplies the output Ssp from the arithmetic element 14a by the output βm (Smor) from the conversion table 13 to weight the high frequency component Ssp.

Next, the arithmetic element 14c adds the original image signal Sorg and the output from the arithmetic element 14b and outputs the processed image signal Sproc. The output processed image signal Sproc is a signal in which the microcalcification portion or the portion where the contrast sharply changes in the high frequency component Ssp of the original image signal is emphasized by the above-described processing.

As described above, according to the image processing apparatus of this embodiment, characteristic image portions such as microcalcification portions are selectively selected while suppressing enhancement of radiation noise within the frequency band of the high frequency component Ssp. Emphasis processing can be performed efficiently, which is very useful in computer-aided image diagnosis and the like.

The calculation by the morphological filter 12 is not limited to the above equation (2), but may be the following equation (3), (4), (5) or (6).

[0086]

(Equation 3)

[0087]

(Equation 4)

[0088]

(Equation 5)

[0089]

(Equation 6)

However, when the calculation of the equation (6) is applied, instead of directly performing the morphology calculation on the high frequency component Ssp which is the output from the calculation element 14a, as shown in FIG. Ssp is converted according to, for example, the second conversion table 15 based on the function graph shown in FIG. 5, and the converted high frequency component f (Ssp) is subjected to a morphological operation by the morphological filter 13 (equation (6)). ) Is desirable.

This is the high frequency component (before conversion) signal S
This is to prevent the occurrence of overshoot or undershoot even when sp is in the range of a predetermined size or more or in the range of a predetermined size or less, and when extreme emphasis is performed.

Further, the conversion table 13 may have the function shape shown in FIG. 3B in addition to that shown in FIG.

[Brief description of drawings]

FIG. 1 is a block diagram showing a first embodiment of an image processing apparatus of the present invention.

FIG. 2 is a matrix showing weighting factors for each pixel in an unsharp mask.

3A and 3B are graphs of functions representing conversion tables.

FIG. 4 is a block diagram showing a second embodiment of the image processing apparatus of the invention.

FIG. 5 is a graph of a function representing a second conversion table.

FIG. 6 is a diagram illustrating a basic operation of a morphological operation.

FIG. 7: Structural element Bi (i = 1, 2, ..., M; M = 4)
Conceptual diagram showing an example

FIG. 8A shows a case where a characteristic image portion to be emphasized exists in an image portion having a uniform peripheral density,
(B) is an explanatory diagram for a case where a characteristic image portion to be emphasized exists in a density gradient with a gentle peripheral density.

FIG. 9 is a conceptual diagram for explaining the operation of the present invention.

FIG. 10 is a graph showing a difference between (A) a processed image signal obtained by the conventional enhancement processing and (B) a processed image signal obtained by the enhancement processing of the present invention.

[Explanation of symbols]

 11 low-pass filter 12 morphological filter 13 conversion table 14a, 14b, 14c arithmetic element 15 second conversion table Sorg original image signal Sus blur mask signal Ssp high-frequency component Sproc processed image signal

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[Procedure amendment]

[Submission date] December 26, 1995

[Procedure amendment 1]

[Document name to be amended] Statement

[Name of item to be corrected] Fig. 10

[Correction method] Change

[Correction contents]

FIG. 10 is a graph showing a difference between (A) a processed image signal obtained by the conventional enhancement processing and (B) a processed image signal obtained by the enhancement processing of the present invention.

Claims (8)

[Claims] 1. An unsharp mask signal Sus corresponding to a predetermined spatial frequency of an original image signal Sorg representing an image is obtained, and the unsharp mask signal Sus and the original image signal Sor.
The difference signal Ssp with respect to g is subjected to a morphological operation using the structuring element Bi and the scale factor λ, so that the difference signal spatially varies less than the structuring element Bi and / or the difference signal. Of the morphology signal Smor showing a characteristic output for the image portion where the change of the image is abrupt, and the enhancement processing according to the morphology signal Smor is performed on the difference signal Ssp so as to enhance the image portion. Characterized image processing method. 2. The image processing method according to claim 1, wherein the enhancement processing according to the morphology signal Smor is enhancement processing according to the following equation (1). [Equation 1] 3. The morphology operation is the following equation (2).
The image processing method according to claim 1 or 2, wherein the calculation is any one of (1) to (5). [Equation 2] (Equation 3) (Equation 4) (Equation 5) [Outside 1] 4. The morphological operation is the following equation (6).
And the morphology signal S
The image processing method according to claim 1, wherein the enhancement processing according to mor is enhancement processing according to the following equation (7). (Equation 6) [Outside 1] (Equation 7) 5. An unsharp mask signal calculation means for obtaining an unsharp mask signal Sus corresponding to a predetermined spatial frequency of an original image signal Sorg representing an image, the unsharp mask signal Sus and the original image signal Sor.
The difference signal Ssp with respect to g is subjected to a morphological operation using the structuring element Bi and the scale factor λ, so that the difference signal spatially varies less than the structuring element Bi and / or the difference signal. Of the morphology signal Smor showing a characteristic output for the image portion where the change of the image is abrupt, and enhancement according to the morphology signal Smor with respect to the difference signal Ssp so as to enhance the image portion. An image processing apparatus comprising: an emphasizing means for performing processing. 6. The image processing apparatus according to claim 5, wherein the enhancement processing by the enhancement means is enhancement processing according to the following expression (1). [Equation 1] 7. The image processing apparatus according to claim 5, wherein the morphology operation by the morphology signal operation means is an operation represented by any one of the following expressions (2) to (5). . [Equation 2] (Equation 3) (Equation 4) (Equation 5) [Outside 1] 8. The morphology operation by the morphology signal operation means is an operation represented by the following expression (6), and the emphasis processing by the emphasis means is an emphasis processing according to the following expression (7). The image processing apparatus according to claim 5. (Equation 6) [Outside 1] (Equation 7)