JPS63268077A - Image processor - Google Patents

Abstract

PURPOSE:To emphasize the density of an image with no conspicuous noise when the image is processed despite the overlap caused between even density parts of images, by calculating the density variance corresponding to the image density and performing weighting to decide a window curve. CONSTITUTION:A dispersion value is obtained by a 1st arithmetic means 4 from the density value of the pixels divided into a matrix form. This obtained dispersion value is added to dispersion data stored in a memory 6 which stores dispersed histogram data corresponding to the density of each pixel via the means 4. Based on the result of this addition, the integrated value of probability which is the sum of these results is calculated by a 2nd arithmetic means 5. Then a window curve is decided, based on said integrated value of probability and the conversion of density is carried out by a 3rd arithmetic means 8. Thus it is possible to decide a weighted window curve in response to evenness of density and to properly emphasize the density of the image.

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Industrial Application Field) The present invention relates to an image processing apparatus that performs window processing on X-ray image data and the like and displays the results as an image.

(Prior art) After converting the subject X-ray information obtained by exposing the subject to X-rays into digital data, treating this as image data and converting it into an appropriate brightness change (brightness level), Image display is performed, and such conversion processing is called window processing. Conventionally, in order to perform such window processing, the operator had to set the window values [window width (WW>, window level (WL)]) via switches etc. provided on the operation panel. It often has disadvantages such as it takes time to set the window value and operation becomes difficult.

Therefore, in order to improve these problems, the applicant of the present invention has previously filed an invention in Japanese Patent Application No. 41933/1983, which is capable of automatically setting window processing conditions without operator intervention. This invention calculates the appearance frequency (histogram) of the image density for the captured image data, obtains an appearance frequency curve as shown in 15 in Figure 8, and then calculates the probability integral as shown in 16 in Figure 8 based on this. After obtaining the curve, this is used as the window processing condition as the wind curve as shown in Fig. 9 to perform the density conversion process of the image data.Each of the above operations is performed multiple times. Wind processing conditions can be automatically set by using the calculation means of
This result can be quickly displayed as an image.

(Problems to be Solved by the Invention) By the way, the processing method for determining the wind curve as shown in FIG. 9 using the probability integral curve 16 obtained based on the appearance frequency curve 15 as in the above invention is as follows. There is a problem. This is because if a large portion of the image has a substantially uniform density, that density area will be greatly emphasized and noise will become noticeable. In other words, as is clear from FIGS. 8 and 9, image density areas with a large number of corresponding pixels tend to be displayed more emphasized, so the wider the area with uniform density, the wider the area. The more these areas become concentrated, the more concentrated they become.

The present invention has been made to address these problems, and provides an image processing device that makes noise less noticeable by appropriately emphasizing density even if the image has a fairly uniform density area. It is intended for that purpose.

[Structure of the Invention] (Means for Solving the Problems) In order to achieve the above object, the present invention is characterized by reducing the number of means for calculating values representing variations in density corresponding to image density for image data. That's what I mean.

(for production) First, a variance value is determined from the S degree value of the pixel.

Subsequently, the dispersion value is added to the dispersion value data (prepared in a separate memory) corresponding to the density of each pixel.

Next, based on this result, a probability integral value of the sum is calculated, a wind curve is determined based on the subsequent probability integral value, and density conversion processing is performed. As a result, it is possible to determine a wind curve that is weighted according to the degree of uniformity of the density, that is, the degree of unevenness of the variation, and therefore it is possible to perform appropriate density emphasis.

(Embodiment) FIG. 1 is a block diagram showing an image processing apparatus according to an embodiment of the present invention, in which path lines (Bus 1. Arithmetic control unit (CTRL>
2. Image memory (IM)3. Dispersion histogram calculation unit (
A1>4 (hereinafter referred to as first calculation means), probability integral value calculation unit (A2) 5 (hereinafter referred to as second calculation means) 9 memory for storing distributed histogram data (MEMl) 6.
It is composed of a probability integral value storage memory (MEM2)'' 72, an image density conversion section (GTA) 8 (third calculation means), a D/A converter (DAC) 9. a display device (DISP>10).

A path line 1 is a line for exchanging data between blocks, and an arithmetic control section 2 is composed of a CPU and the like and controls the overall operation.

The image memory 3 is a memory for storing various image data including X-ray information and the like.

When image data is input, the first calculation means 4 converts this image data into a plurality of blocks, for example, 64 blocks 81, B2, 83, . . . as shown in FIG.

Each block is divided into 864 blocks, and each block has a plurality of pixels, for example, 16 pixels P1 and P, as shown in FIG.
2, P3, ..., B16, first each block 81, B2, B3,
For . That is, the variance value is calculated from the density values of 16 pixels in one block. Next, the first calculating means 4 adds the dispersion value to dispersion data (stored in a dispersion histogram data storage memory 6 to be described later) corresponding to the density of each pixel of each block. That is, the sum of values representing variations in each image density is calculated.

The dispersion histogram data storage memory 6 stores the calculation results by the first calculation means 4. This is, for example, the fourth
They are stored in the correspondence relationship shown in the figure. Note that this memory 6 is initially set to an initial state in which the contents are cleared.

The second calculation means 5 reads the data stored in the memory 6, and calculates the total sum IVj of the dispersed phases of the dispersed phases V1 corresponding to the pixel density value di (N is taken J=1, the obtained pixel density) and the number of pixel density. ) is calculated as follows.

ΣVJ j=1 Then, the sum 3i of Pl from the minimum pixel density to the current pixel density is calculated for all pixel densities as shown in the following equation.

3i=de Pi (1≦i≦N) J=1 That is, the probability integral (direct) of the sum of values representing the variations in the respective edge densities is calculated.

The probability integral value storage memory 7 stores the calculation result by the second calculation means 5. This is stored in a correspondence relationship as shown in FIG. 5, for example. - As an example, a case where an image is constructed with 8 bits is shown.

When the image data is input, the third calculation means 8 reads out the data stored in the memory 7, performs density conversion processing, and calculates the pixel density value to be displayed according to the pixel density value of the image. The image is output to the A converter 9 and the corresponding image is displayed on the first display device 10.

Next, the operation of this embodiment will be explained.

The image data stored in the image memory 3 is subjected to calculation 1ti.
The signal is input to the first calculation means 4 under the control of the l control section 2. The first calculation means 4 divides the image data into a plurality of blocks as shown in FIG. 2, and calculates a variance value from the density value for each pixel in each block as shown in FIG.

Next, the variance value is added to the variance data in the memory 6 corresponding to the density of each pixel in each block, and a sum indicating the variation in each image density is calculated using the correspondence relationship as shown in FIG. This sum 11 is shown in FIG. Subsequently, the second calculation means 5 reads data from the memo 1) 6 and calculates the probability integral value of the sum of the values representing the dispersion of each image D8 degrees in the correspondence relationship as shown in FIG. FIG. 6 shows this probability integral curve 12.

The third calculation means 8 reads out the data from the memory 7 in which the probability integral value data is stored, determines the wind curve 13 based on the probability integral curve 12 as shown in FIG. 7, and performs the density conversion process. The image is displayed on the display device 10 via the D/A converter 9. At the moment the displayed concentration is O.
If 255 to 255 (8 bits) is entered, the 1st shift obtained by multiplying 3i by 255 becomes the display target.

According to this embodiment, probability integral curve 1 in FIG.
2, the wind curve 13 shown in FIG. Therefore, even if there is a wide area with uniform density, appropriate density emphasis will be performed without extreme density enhancement.Therefore, noise will not be noticeable. , diagnostic ability can be improved.

In the embodiment, the dispersion value is shown as the value representing the variation in concentration, but the standard deviation value may also be used. Alternatively, the absolute value of the data difference between adjacent pixels may be used. In short, any value can be used as long as it can represent the amount of density variation. Furthermore, in the first calculation means 4 in FIG. 1, one pixel of the image is made to correspond to the variance value of the block containing it, but it is also possible to make the variance value within the block correspond to the average density value of the block. good. The calculation target does not need to be the entire image,
It may be a part of it. Furthermore, it is not always necessary to divide the data into blocks.

[Advantageous Effects of the Invention 1] As described above, according to the present invention, it is possible to perform appropriate density enhancement even if there are many areas with uniform density in an image, so that it is possible to prevent noise from becoming noticeable. .

[Brief explanation of drawings]

Fig. 1 is a block diagram showing an image processing device according to an embodiment of the present invention, Figs. 2 and 3 are data arrangement diagrams showing the principle of the invention, and Figs. 4 and 5 are data calculation examples of this embodiment. FIGS. 6 and 7 are characteristic curve diagrams obtained by this embodiment, and FIGS. 8 and 9 are characteristic curve diagrams of the conventional example. 4...Dispersion histogram calculation unit (first calculation means),
5... Probability integral value calculation unit (second calculation means), 6...
- Memory for storing dispersion histogram data, 7... Memory for storing probability integral values, 8... Image density conversion unit (third calculation means). Agent Patent Attorney Nori Ken Ken Kanshu Takeshi Kondo Figure 1 Figure 2 1 ≧Rs Figure 3 = AW Spiritual Materials Survey Sumono

Claims (3)

[Claims] (1) In an image processing device that performs window processing on image data and displays the result as an image, the first calculation means calculates a value representing density variation corresponding to image density for the image data; a second calculation means for calculating a probability integral value of the sum of values representing variations in image density based on the output of the calculation means; and a density conversion process for the image data based on the output of the second calculation means. An image processing device characterized by comprising: third calculation means for performing the calculation. (2) The image processing device according to claim 1, wherein the value representing the variation in image density is a variance or a standard deviation. (3) The image processing device according to claim 1, wherein the value representing the variation in image density is the absolute value of the difference in data between adjacent pixels forming the image.