JPH0962836A - Picture processing method and picture processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a clear picture by suppressing or preventing edge emphasis for an edge area whose degree of an edge is large and executing edge emphasis for an area which is not large. SOLUTION: A medical picture processor 100 is provided with an operation part 1 receiving the instruction and the selection of an operator, a system control part 2 controlling the operation of respective parts, an image data storage part 3 storing an original medical picture and a new medical picture obtained by picture-processing the original picture, a picture processing part 4 executing an excessive emphasis preventing edge emphasis processing and a display part 5 displaying the medical picture. In such a case, the smoothing picture element value is obtained by using an original picture element value, the picture element value of a picture element near a notice picture element and a smoothing space filter. Then, a differential picture element value is obtained by subtracting the smoothing picture element value from the original picture element value. It is judged whether the degree of the edge is large or not from the differential picture element value. Edge emphasis is not executed on the picture element whose degree of the edge is large. Edge emphasis is executed on the picture element whose degree is not large.

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 an image processing apparatus, and more particularly to an image processing method and an image processing apparatus capable of preventing over-enhancement when applying edge emphasis to an original image. Especially X-ray CT
This is useful when edge enhancement is applied to an original medical image acquired by scanning a subject with a medical image diagnostic apparatus such as a (Computed Tomography) apparatus.

[0002]

2. Description of the Related Art FIG. 22 is an exemplary view of an original medical image ORI obtained by scanning a lung field of a subject by an X-ray CT apparatus. In this original medical image ORI, the edge region G corresponding to the pleura has a large degree of edge (a large difference in pixel value from the surroundings), and thus is relatively clear. In contrast, the fine trachea and texture inside the lungs (Texture)
The edge area L corresponding to the component is relatively unclear because the degree of edge is small (the difference in pixel value from the surroundings is small).

FIG. 23 shows an edge-enhanced medical image E obtained by subjecting the original medical image ORI to edge-enhancement processing.
It is an illustration figure of EI. The edge region L corresponding to the fine trachea and texture component inside the lung is the original medical image ORI.
Was relatively unclear, but edge-enhanced medical image EE
I is relatively clear. On the other hand, the edge region G corresponding to the pleura is overemphasized because the degree of the original edge is large.

[0004]

However, if the edge region G corresponding to the pleura is overemphasized, it seems as if an air layer is present in that region, which makes it indistinguishable from pneumothorax for diagnostic purposes. There is a problem. In addition, not only medical images, but generally overemphasized is not preferable. Therefore, an object of the present invention is to provide an image processing method and an image processing apparatus capable of preventing edge enhancement and applying edge enhancement to an image.

[0005]

According to a first aspect of the present invention, the present invention extracts an edge region in an original image, and extracts an edge region having a degree of edge larger than a predetermined threshold value from other regions. There is also provided an image processing method characterized by performing edge enhancement which is also suppressed or not performing edge enhancement. In the image processing method according to the first aspect described above, the suppressed edge emphasis is applied to the edge area having a large degree of edge or the edge emphasis is not applied, and the edge area having a small degree of edge is edge emphasized. Therefore, for example, the original image OR obtained by scanning the lung field
In I, the edge region G corresponding to the pleura with a large degree of the original edge is not overemphasized. On the other hand, the edge region L corresponding to the fine trachea or texture component inside the lung where the degree of the original edge is not large is properly edge-emphasized.

In the above configuration, the edge area is extracted by obtaining the difference image between the smoothed image obtained by performing the smoothing process on the original image and the original image, or by performing the edge enhancement process on the original image. It is preferable to extract the edge region by obtaining a difference image between the obtained edge-emphasized image and the original image.

Further, in the above structure, for the pixels having the difference pixel value larger than the predetermined edge degree threshold value among the pixels of the difference image, the original pixel value of the corresponding pixel of the original image is used, and the difference pixel value which is not large is used. For pixels with, the product obtained by multiplying the difference pixel value by the emphasis gain is the original pixel value or the smoothed pixel value (pixel value in the smoothed image)
It is preferable to generate a new image using the calculated pixel value added to.

Further, in the above-mentioned configuration, among the pixels of the difference image, for the pixels having the difference pixel value larger than the predetermined edge degree threshold value, the average pixel value and the difference pixel value obtained respectively by a plurality of line filters in different directions. The second calculated pixel value obtained by adding the product obtained by multiplying the difference of the absolute values of which has the smallest absolute value by the emphasis gain to the original pixel value or the smoothed pixel value (pixel value in the smoothed image) is used. ,
For a pixel having a difference pixel value that is not large, the first operation pixel value obtained by adding the product obtained by multiplying the difference pixel value by the emphasis gain to the original pixel value or the smoothed pixel value is used.
It is preferable to generate a new image.

According to a second aspect, the present invention relates to an edge area extracting means for extracting an edge area in an original image, and an edge area having a degree of edge larger than a predetermined threshold value, as compared with other areas. There is provided an image processing apparatus comprising: an edge enhancement means for preventing over-enhancement, which applies a suppressed edge enhancement or does not apply an edge enhancement.

According to the image processing apparatus of the second aspect,
The image processing method according to the first aspect can be suitably implemented.

According to a third aspect of the present invention, in the image processing apparatus having the above structure, the edge area extracting means obtains a smoothed image obtained by performing a smoothing process on the original image and a difference image between the original image. Alternatively, there is provided an image processing device characterized by obtaining a difference image between an edge-enhanced image obtained by subjecting an original image to edge-enhancement processing and the original image. It is possible to extract the edge region from the edge-enhanced image obtained by performing the edge-enhancement process on the original image, but when the edge region is extracted from the difference image between the smoothed image and the original image or the edge-enhanced image and the difference image between the original image, It is possible to properly extract an edge region having a large degree of edge without being largely affected by the variation in the original image.

According to a fourth aspect of the present invention, in the image processing apparatus having the above-mentioned configuration, the over-emphasizing prevention edge enhancing means is a pixel having a difference pixel value larger than a predetermined edge degree threshold value among the pixels of the difference image. For the original image, the original pixel value of the corresponding pixel of the original image is used, and for a pixel having a difference pixel value that is not large, the product obtained by multiplying the difference pixel value by the emphasis gain is used as the original pixel value or the smoothed pixel value ( Using the calculated pixel value added to (pixel value in smoothed image),
Provided is an image processing device characterized by generating a new image. In the image processing method according to the fourth aspect, the edge enhancement is not performed on the edge region having a large degree of edge, and the edge enhancement is performed on the edge region having a small degree of edge. As a result, it is possible to prevent excessive emphasis and apply edge emphasis.

According to a fifth aspect of the present invention, in the image processing apparatus having the above structure, the over-emphasizing prevention edge emphasizing means is a pixel having a difference pixel value larger than a predetermined edge degree threshold value among the pixels of the difference image. Regarding the difference between the average pixel value obtained by a plurality of line filters in different directions and the difference pixel value, the product obtained by multiplying the one with the smallest absolute value by the emphasis gain is the original pixel value or the smoothed pixel. The second operation pixel value added to the value (pixel value in the smoothed image) is used, and for a pixel having a difference pixel value that is not large, the product obtained by multiplying the difference pixel value by the emphasis gain is the original pixel value. Alternatively, there is provided an image processing device characterized in that a new image is generated using the first calculated pixel value added to the smoothed pixel value. In the image processing method according to the fifth aspect, relatively weak edge enhancement is applied to edge regions having a large degree of edge, and relatively strong edge enhancement is applied to edge regions having a small degree of edge. As a result, it is possible to prevent excessive emphasis and apply edge emphasis.

[0014]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in more detail with reference to the embodiments shown in the drawings. The present invention is not limited to this.

First Embodiment FIG. 1 is a block diagram showing a medical image processing apparatus 100 according to the first embodiment of the present invention. This medical image processing apparatus 1
00 is an operation unit 1 that receives an operator's command and selection,
A system control unit 2 that controls the operation of each unit, an image data storage unit 3 that stores an original medical image and a new medical image obtained by image processing of the original medical image, and an edge enhancement process that prevents over-enhancement described below are performed. The image processing unit 4 and the display unit 5 for displaying a medical image are provided.

FIG. 2 is a flow chart showing the edge enhancement processing for preventing over-enhancement by the medical image processing apparatus 100.
In step ST1, one pixel in the original medical image obtained by scanning the subject with the medical image diagnostic apparatus is focused on. Here, as shown in FIG. 3, an original pixel value table ORT that schematically represents an original medical image by an array of pixel values.
Is assumed as the original medical image. This original pixel value table O
In RT, i and j represent the coordinates of each pixel, and the numerical values in the figure represent pixel values. These pixel values are called original pixel values. Although the original pixel value is set to an appropriate value in FIG. 3, it is actually a CT value or a screen brightness value.

In step ST2, a smoothed pixel value is obtained using the original pixel value, the pixel value of a pixel in the vicinity of the pixel of interest, and the smoothed spatial filter. FIG. 4 illustrates the smoothing spatial filter M (K, L). Further, FIG. 5 shows the original pixel value table ORT of FIG. 3 and the smoothing spatial filter M of FIG.
The smoothed pixel value table SMT obtained by performing the convolution calculation of (K, L) is shown. When calculating the smoothed pixel value of the outermost peripheral pixel of the original pixel value table ORT of FIG. 3, consider virtual pixels around the outermost periphery of the original pixel value table ORT of FIG. As the value, the pixel value of the nearest real pixel (pixel in the original pixel value table ORT in FIG. 3) was used.

At step ST3, the difference pixel value is obtained by subtracting the smoothed pixel value from the original pixel value. FIG. 6 illustrates a difference pixel value table DFT obtained by subtracting the smoothed pixel value table SMT of FIG. 5 from the original pixel value table ORT of FIG.

In step ST4, it is determined from the difference pixel value of the pixel of interest whether the edge degree is large. If the edge degree is large, proceed to step ST5,
If the edge degree is not large, the process proceeds to step ST6.
For example, if the difference pixel value is more negative than the edge degree threshold “-9”, the edge degree is determined to be large, and otherwise the edge degree is not determined to be large. In this case, for the pixels in the shaded area in the difference pixel value table DFT of FIG. 6, the process proceeds to step ST5, and for the other pixels, the process proceeds to step ST6. In step ST5, the original pixel value is used as the pixel value of the pixel in the new medical image corresponding to the pixel of interest. That is, the edge emphasis is not applied to the pixels having a large edge degree. Then, the process proceeds to step ST7. In step ST6, the result of multiplying the difference pixel value by a predetermined enhancement gain and then adding it to the smoothed pixel value is used as the pixel value of the pixel in the new medical image corresponding to the pixel of interest. That is, the edge enhancement is applied to the pixels whose edge degree is not large. In step ST7, steps ST1 to ST6 are executed for all pixels of the original medical image.

FIG. 7 is a selection emphasized pixel value table SET representing a new medical image obtained as a result of performing the above-described overemphasized prevention edge emphasis process on the original pixel value table ORT of FIG. The emphasis gain was set to "4". Further, the name "selection-emphasized pixel value" means a pixel value obtained by selectively applying edge emphasis. The original pixel values are used for the pixel values of the shaded pixels in the selective emphasis pixel value table SET, and the results of the edge emphasis calculation are used for the pixel values of the other pixels.

FIG. 8 shows the original medical image ORI shown in FIG.
It is an illustration figure of the new medical image SEI obtained by performing the said excessive emphasis prevention edge emphasis process in FIG. In this new medical image SEI, the edge regions L corresponding to the fine trachea and texture components inside the lung, which were relatively unclear in the original original medical image ORI, are relatively clear. On the other hand, the edge region G corresponding to the pleura with a high degree of original edge
Since no edge enhancement is applied, is not over-emphasized and is relatively clear as in the original.

-Second Embodiment- FIG. 9 is a block diagram showing a medical image processing apparatus according to a second embodiment of the present invention. This medical image processing apparatus 200
Is an operation unit 1 that receives commands and selections from an operator, a system control unit 2 that controls the operation of each unit, image data that stores an original medical image and a new medical image obtained by image processing the original medical image. The storage unit 3 includes an image processing unit 14 that performs an overemphasized edge prevention process, which will be described later, and a display unit 5 that displays a medical image.

FIG. 10 is a flow chart showing the medical image processing by the medical image processing apparatus 200. Step SU
In 1, the attention is paid to one pixel in the original medical image obtained by scanning the subject with the medical image diagnostic apparatus. Here, the original pixel value table ORT shown in FIG. 3 is assumed as an original medical image. In step SU2, the edge emphasis pixel value is obtained using the original pixel value, the pixel value of the pixel in the vicinity of the pixel of interest, and the edge emphasis spatial filter. 11 and 12 illustrate the edge emphasis spatial filter F (K, L). Further, FIG. 13 shows an edge-enhanced pixel value table EE obtained by performing a convolution operation on the original pixel value table ORT of FIG. 3 and the smoothing spatial filter F (K, L) of FIG.
Indicates T.

In step SU3, the edge emphasis pixel value is subtracted from the original pixel value to obtain the difference pixel value. FIG. 14 illustrates a difference pixel value table DFT obtained by subtracting the edge-enhanced pixel value table EET of FIG. 13 from the original pixel value table ORT of FIG.

In step SU4, it is determined from the difference pixel value of the pixel of interest whether the edge degree is large. If the edge degree is large, proceed to step SU5,
If the edge degree is not large, the process proceeds to step SU6.
For example, if the difference pixel value is negative than the edge degree threshold value “−49”, it is determined that the edge degree is large,
Otherwise, it is determined that the edge degree is not large. In this case, for the pixels in the shaded area in the difference pixel value table DFT of FIG. 14, the process proceeds to step SU5, and for the other pixels, the process proceeds to step SU6. In step SU5, the difference pixel value is replaced with the smallest absolute value of the differences between the average pixel value and the difference pixel value obtained by the line filters in different directions. This calculation corresponds to averaging the difference pixel values in the direction having the strongest connectivity. FIG.
The line filters L (K, L) in the four directions are illustrated in FIG. Further, FIG. 16 illustrates the replaced difference pixel value table DEF ′. In the example of FIG. 14, the line filter having the smallest absolute value is the pixel value “−5” in the shaded area.
For "0", the pixel value in the shaded area is "-80", and for the pixel value "-60" in the shaded area, the pixel value is "-80" in the shaded area. 15A corresponds to that of FIG. In step SU6, the result of multiplying the difference pixel value by a predetermined enhancement gain and then adding the result to the original pixel value is used as the pixel value of the pixel in the new medical image corresponding to the pixel of interest. As a result, normal edge enhancement is applied to pixels whose edge degree is not large. On the other hand, for pixels with a large edge degree, the difference pixel values are averaged in the connecting direction, and thus suppressed edge enhancement is performed. In step SU7, the above-mentioned step SU is performed for all pixels of the original medical image.
1 to SU6 are executed.

FIG. 17 shows the original pixel value table ORT shown in FIG.
9 is a selective emphasis pixel value table SET representing a new medical image obtained as a result of performing the over-enhancement prevention edge emphasis processing for. The emphasis gain was set to "1". Further, the name "selection-emphasized pixel value" means a pixel value obtained by selectively performing normal edge emphasis and suppressed edge emphasis. The pixel values of the shaded pixels in the selective emphasis pixel value table SET are the results of the suppressed edge emphasis calculation, and the pixel values of the other pixels are the results of the edge emphasis calculation.

FIG. 18 shows the original medical image OR shown in FIG.
It is an illustration figure of the new medical image SEI obtained by performing the said excessive emphasis prevention edge emphasis process to I. In this new medical image SEI, the edge regions L corresponding to the fine trachea and texture components inside the lung, which were relatively unclear in the original original medical image ORI, are relatively clear. On the other hand, since the edge region G corresponding to the pleura having a high degree of original edge is subjected to the suppressed edge emphasis, it is not over-emphasized and is relatively clear as in the original.

Although the line filter shown in FIG. 15 (the number of directions = 4, the number of pixels = 3) is used in the second embodiment, the line filter shown in FIG. 19 (the number of directions = 4, the number of pixels =) is used. 5) or the line filter shown in FIG. 20 (the number of directions =
8, the number of pixels = 5) may be used.

-Modified Embodiment- FIG. 21 is a flowchart showing image processing for removing random noise from an original image. The original image signal is passed through an edge enhancement filter to generate an edge enhanced image (step U
1). A high frequency component image is generated by subtracting the original image signal from the edge emphasized image (step U2). Since the edge-enhanced image is a change in the original image signal, that is, the high-frequency component is emphasized, when the original image signal is subtracted from the edge-enhanced image, only the high-frequency component image remains. The high-frequency component image is passed through line filters in multiple directions to remove edge / texture components and extract random noise components (step U3). Since the high frequency component image is composed of random noise and edge texture components in the high frequency region, when the edge texture component is removed by a line filter having a plurality of directions, only the random noise component remains. Line filters with multiple orientations are morphographic filters (morpho g
It is a collection of low-pass filters that work in each direction. Here, morphography means that the shape of the kernel is changed in conformity with the shape of the object. Random noise is subtracted from the original image signal to obtain an output image signal (step U4).
As a result, random noise in the high frequency region is removed from the output image signal, and an easy-to-see image is obtained.

[0030]

According to the image processing method and the image processing apparatus of the present invention, the edge enhancement is suppressed or not performed on the edge region having a large degree of edge, and the edge enhancement is performed on the region having a small degree of edge. By doing
Edge enhancement can be performed while preventing over-enhancement, and an image that is easy to see can be obtained. Particularly, in the case of a medical image, it is highly useful because an image convenient for diagnosis can be obtained.

[Brief description of drawings]

FIG. 1 is a configuration diagram showing a medical image processing apparatus according to a first embodiment of the present invention.

FIG. 2 is a flow diagram showing an overemphasized prevention edge enhancement process by the medical image processing apparatus of FIG.

FIG. 3 is an exemplary diagram of an original pixel value table in which an original image obtained by scanning an object with an X-ray CT apparatus is expressed in a pixel value table format.

FIG. 4 is an explanatory diagram of an example of a smoothing spatial filter.

5 is an exemplary diagram of an original pixel value table of FIG. 3 and a smoothed pixel value table generated from the smoothed spatial filter of FIG.

6 is an exemplary diagram of a difference pixel value table obtained by subtracting the smoothed pixel value table of FIG. 5 from the original pixel value table of FIG.

FIG. 7 is a view showing an example of a selection emphasized pixel value table obtained by performing the excessive emphasis prevention edge emphasis process of FIG. 2 on the original pixel value table of FIG.

FIG. 8 is a view showing an example of a selection emphasized image obtained by performing the excessive emphasis prevention edge emphasis process of FIG. 2 on the original image of FIG. 22.

FIG. 9 is a configuration diagram showing a medical image processing apparatus according to a second embodiment of the present invention.

10 is a flowchart showing an overemphasized prevention edge enhancement process by the medical image processing apparatus of FIG.

FIG. 11 is an explanatory diagram of an example of an edge emphasis spatial filter.

FIG. 12 is an explanatory diagram of another example of the edge emphasis spatial filter.

13 is an exemplary diagram of an original pixel value table of FIG. 3 and an edge-enhanced pixel value table generated from the edge-enhanced spatial filter of FIG.

14 is an exemplary diagram of a difference pixel value table obtained by subtracting the edge emphasis pixel value table of FIG. 13 from the original pixel value table of FIG.

FIG. 15 is an explanatory diagram of an example of a line filter.

16 is an exemplary diagram of a replaced difference pixel value table generated from the difference pixel value table of FIG.

FIG. 17 is a view showing an example of a selection emphasized pixel value table obtained by performing the excessive emphasis prevention edge emphasis process of FIG. 10 on the original pixel value table of FIG.

18 is an exemplary diagram of a selection-emphasized image obtained by performing the over-enhancement prevention edge enhancement processing of FIG. 10 on the original image of FIG. 22.

FIG. 19 is an explanatory diagram of another example of the line filter.

FIG. 20 is an explanatory diagram of still another example of the line filter.

FIG. 21 is a flowchart of image processing capable of removing random noise in a high frequency region to obtain a medical diagnostic image that is easy to see.

FIG. 22 is an exemplary view of an original image obtained by scanning a subject with an X-ray CT apparatus.

23 is an exemplary diagram of an edge-enhanced image generated from the original image of FIG. 22 and the edge-enhanced spatial filter of FIG. 11.

[Explanation of symbols]

 100,200 Medical image processing apparatus 1 Operation unit 2 System control unit 3 Image data storage unit 4,14 Image processing unit 5 Display unit

Front page continued (72) Inventor Shigeru Matsumura 127 GE Yokogawa Medical Systems Co., Ltd. 4-7 Asahigaoka, Hino City, Tokyo

Claims (5)

[Claims] 1. An edge region in an original image is extracted, and an edge region having a greater degree of edges than a predetermined threshold is subjected to edge enhancement that is suppressed more than other regions, or no edge enhancement is performed. An image processing method characterized by the above. 2. An edge area extraction means for extracting an edge area in an original image, and edge enhancement with a degree of edge greater than a predetermined threshold is suppressed more than other areas. An image processing apparatus, comprising: an overemphasized prevention edge enhancement unit that does not perform edge enhancement. 3. The image processing apparatus according to claim 2, wherein the edge area extracting unit obtains a difference image between a smoothed image obtained by performing a smoothing process on the original image and the original image,
Alternatively, an image processing apparatus characterized by obtaining a difference image between an edge-enhanced image obtained by subjecting the original image to edge-enhancement processing and the original image. 4. The image processing apparatus according to claim 3, wherein the over-enhancement prevention edge enhancement means selects pixels of a difference image having a difference pixel value larger than a predetermined edge degree threshold from the original image. The original pixel value of the corresponding pixel is used, and for a pixel having a difference pixel value that is not large, the product obtained by multiplying the difference pixel value by the emphasis gain is the original pixel value or the smoothed pixel value (the pixel in the smoothed image. An image processing device characterized by generating a new image using the calculated pixel value added to (value). 5. The image processing device according to claim 3, wherein the over-enhancement prevention edge enhancement means sets pixels having different pixel values larger than a predetermined edge degree threshold value among pixels of the difference image in different directions. The original pixel value or the smoothed pixel value (smoothed image) is the product obtained by multiplying the difference between the average pixel value and the difference pixel value obtained by a plurality of line filters, whichever has the smallest absolute value, by the emphasis gain. Pixel value in the second calculation pixel value added to
For a pixel having a difference pixel value that is not large, the first operation pixel value obtained by adding the product obtained by multiplying the difference pixel value by the emphasis gain to the original pixel value or the smoothed pixel value is used.
An image processing device characterized by generating a new image.