KR101027622B1 - Smoothing filtering apparatus and method for the same - Google Patents

Abstract

A smoothing filtering device according to an embodiment of the present invention includes a derivative unit for generating a differential image by differentiating an original image, a filter unit for filtering the differential image using a reference value for the generated differential image, and the filtered differential image It includes an integral part that integrates.

Smoothing filtering, ultrasound image, speckle noise, edge contrast, differential filter, reverse filter, denoising

Description

Smoothing filtering device and method {SMOOTHING FILTERING APPARATUS AND METHOD FOR THE SAME}

Embodiments of the present invention relate to a smoothing filtering apparatus and method.

Speckle noise included in the ultrasound B-mode image not only degrades the image quality but also important morphological features such as the edge contrast of body organs and anatomical features to be imaged. Decreases accuracy Computed Tomography (CT) images contain a lot of noise, although their effects are relatively less because they are higher quality images than ultrasound images. This can be a serious obstacle in terms of image processing.

At present, many efforts have been made to reduce noise in ultrasound images and CT images. Among them, a TV (Total Variation) based method is known as a method of effectively removing noise while significantly maintaining the edge contrast of anatomical features of body organs to be imaged.

However, in the case of TV-based methods, significantly improved ultrasound images can be obtained, but there are some unresolved problems.

First, since the TV filter requires mathematical modeling of noise according to the characteristics of the image, it is difficult to apply the same to the images having various characteristics. For example, in the case of an ultrasound image, the speckle noise is mathematically modeled to reduce TV (Total Variation) based on the same. At this time, if the characteristics of the image are different, a problem of applying a different noise model may occur.

Secondly, the TV-based method defines an energy function and optimizes iteration in the direction of minimizing it. In general, the optimization method has a problem of falling into local minima. In addition, TV-based methods have different results depending on the number of iterations, which can significantly slow down the processing speed.

Third, while the TV-based method can obtain a very noise-free image, there may be a problem that the shading and shadowing phenomenon remains in the image.

Embodiments of the present invention aim to be able to improve the speed of denoising.

Embodiments of the present invention aim to effectively remove noise while maintaining edge contrast of anatomical features of body organs based on gradient values of an image.

Embodiments of the present invention aim to remove shading and shadowing from an image.

The object of the present invention is not limited to the above-mentioned objects, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

Smoothing filtering device according to an embodiment of the present invention comprises a derivative for generating a differential image by differentiating the original image; A filter unit to filter the differential image using a reference value for the generated differential image; And an integrator that integrates the filtered differential image.

Smoothing filtering method according to an embodiment of the present invention comprises the steps of: generating a differential image by differentiating an original image; Filtering the differential image using a reference value for the generated differential image; And integrating the filtered differential image.

Specific details of other embodiments are included in the detailed description and the accompanying drawings.

Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various different forms, and only the embodiments make the disclosure of the present invention complete, and those skilled in the art to which the present invention pertains. It is provided to fully inform the person having the scope of the invention, which is defined only by the scope of the claims. Like reference numerals refer to like elements throughout.

According to embodiments of the present invention, it is possible to improve the speed of denoising.

According to embodiments of the present invention, the noise may be effectively removed while maintaining the edge contrast of the anatomical feature points of the body organs based on the gradient value of the image.

According to embodiments of the present invention, shading and shadowing may be removed from an image.

According to embodiments of the present invention, by introducing a thresholding technique independent of image characteristics, the same threshold value may be applied to images having various characteristics.

According to embodiments of the present invention, the result may be improved when the feature-based feature extraction is performed.

According to embodiments of the present invention, it is possible to easily determine the exact anatomical structure.

According to embodiments of the present invention, it is possible to implement surface rendering of a three-dimensional (3D) ultrasound image.

Hereinafter, with reference to the accompanying drawings will be described embodiments of the present invention;

1 is a block diagram illustrating a smoothing filtering apparatus according to an embodiment of the present invention. Here, the smoothing filtering device may be mounted on an ultrasound diagnostic device.

Referring to FIG. 1, the smoothing filtering device 100 includes a smoothing unit 110, a differential unit 120, a calculation unit 130, a filter unit 140, an integrating unit 150, and a control unit 160. can do.

The smoothing unit 110 may smooth the original image. In this case, the smoothing unit 110 may apply a smoothing filter to the original image to smooth the original image.

As described above, the smoothing unit 110 may generate a smoothing image, which is a softer image than the original image, by performing a smoothing process for emphasizing low frequency components of the signal related to the original image.

Here, the original image is an image included in the ultrasound B-mode image, and may refer to an image that has not been processed by any processing. In addition, the smoothing filter may include a Gaussian filter, a median filter, a bilateral filter, a mean filter, or the like.

The derivative unit 120 may generate a differential image by differentiating the smoothing image. In this case, the derivative unit 120 may generate the differential image by applying a differential filter to the smoothing image. In addition, the differential unit 120 may differentiate the smoothing image in the X-axis direction and the Y-axis direction, respectively, to generate a first differential image and a second differential image as the differential image.

Here, the differential filter may include a sobel filter, a prewitt filter, a Roberts filter, a LoGlac filter of the Laglacian filter.

For example, the derivative unit 120 may generate an image (dx image) of a horizontal component as the first differential image by differentiating the smoothing image in the X-axis direction using the differential filter. In addition, the derivative unit 120 may generate a vertical component image (dy image) as the second differential image by differentiating the smoothing image in the Y-axis direction using the differential filter.

As described above, the derivative unit 120 may measure a gradient value by differentiating the smoothing image in the X and Y-axis directions, respectively. In this way, the derivative unit 120 may provide an environment capable of effectively removing noise while maintaining edge contrast of anatomical feature points of body organs based on the measured gradient values.

The calculator 130 may calculate the first threshold of the first differential image by thresholding the first differential image. In addition, the calculator 130 may calculate the second threshold value of the second differential image by thresholding the second differential image.

Alternatively, the calculation unit 130 obtains a first average value and a first variance value for the first differential image, adds the first average value and the first variance value (first average value + first variance value), and The resulting first total value can be calculated. In addition, the calculation unit 130 obtains a second average value and a second dispersion value for the second differential image, and adds the second average value and the second dispersion value (second average value + second dispersion value) as a result. The second total value of can be calculated.

In this case, the calculator 130 may obtain the first average value and the first dispersion value within a window having a predetermined size centering on each pixel in the first differential image. In addition, the calculation unit 130 may obtain the second average value and the second variance value within a window of a predetermined size centering on each pixel in the second differential image.

The filter unit 140 may filter the differential image by using a reference value for the generated differential image. That is, the filter unit 140 may filter the first differential image by using a first reference value for the first differential image. In addition, the filter unit 140 may filter the second differential image by using a second reference value for the second differential image.

To this end, the filter unit 140 may set the first threshold value as the first reference value and set the second threshold value as the second reference value. Accordingly, the filter unit 140 may filter a first differential image corresponding to a value less than or equal to the first threshold value among the values of the first differential image, and, among the values of the second differential image, The second differential image corresponding to a value less than or equal to two thresholds may be filtered.

Alternatively, the filter unit 140 may set the first total value (first average value + first dispersion value) as the first reference value, and set the second total value (second average value + second dispersion value). It can set as said 2nd reference value. Accordingly, the filter unit 140 may filter a first differential image corresponding to a value less than or equal to the first total value among the values of the first differential image, and, among the values of the second differential image, The second differential image corresponding to a value less than or equal to 2 total values may be filtered.

The integrator 150 may integrate the filtered first differential image and the filtered second differential image, respectively. In this case, the integrator 150 may integrate the filtered first differential image and the filtered second differential image by using a reverse filter.

Here, the reverse filter may be implemented to make the differential filter 1, that is, delta. Hereinafter, a reverse filtering process performed by the reverse filter will be described in detail with reference to Equation.

를 미분 필터가 적용된 수평 혹은 수직 성분의 이미지(dx,dy)라 하고,

를 미분 필터가 적용되기 전의 원본 이미지라고 전제한다. 이러한 경우,

을 구하기 위해서는 하기 수학식 1의 슈도-인버스 솔루션(pseudo-inverse solution)을 이용할 수 있다.In this embodiment

Is called the horizontal or vertical image (dx, dy) with the differential filter applied,

Let is the original image before the differential filter is applied. In this case,

To obtain the Pseudo-inverse solution of the equation (1) can be used.

은

의 리버스 필터(reverse filter)를 가리킨다. 즉,

와

은 하기의 수학식 2와 같이 표현될 수 있다.Where '*' indicates convolution,

silver

Pointer to the reverse filter for. In other words,

Wow

May be expressed as Equation 2 below.

In addition, g of Equation 1 indicates a normalization function that is a solution of Equation 3 below.

As described above, according to embodiments of the present invention, the original image shown in FIG. 2 may be processed into the smoothed image shown in FIG. 3. That is, as shown in FIG. 2, the original image lacks important morphological accuracy, such as edge contrast of anatomical features of body organs.

On the other hand, the image of FIG. 3 to which the embodiments of the present invention are applied not only removes noise while maintaining the edge contrast and the like, but also removes shading and shadowing.

For reference, FIG. 2 is a diagram illustrating an example of an original image before embodiments of the present invention are applied, and FIG. 3 is a diagram illustrating an example of an image after embodiments of the present invention are applied.

4 is a flowchart illustrating a smoothing filtering method according to an embodiment of the present invention. The smoothing filtering method may be implemented by the smoothing filtering device 100 of FIG. 1.

Referring to FIG. 4, in operation S410, the smoothing filtering device may smooth the original image. In this case, the smoothing filtering device may apply a smoothing filter to the original image to smooth the original image.

As described above, the smoothing filtering device may generate a smoothing image that is a softer image than the original image by performing a smoothing process for emphasizing low frequency components of the signal related to the original image.

Here, the original image may be an image included in the ultrasound B-mode image, and may refer to an image which has not been processed. In addition, the smoothing filter may include a Gaussian filter, a median filter, a bilateral filter, a mean filter, or the like.

Next, in operation S420, the smoothing filtering apparatus may generate a differential image by differentiating the smoothing image. In this case, the smoothing filtering device may generate the differential image by applying a differential filter to the smoothing image. In addition, the smoothing filtering apparatus may differentiate the smoothing image in the X-axis direction and the Y-axis direction, respectively, to generate a first differential image and a second differential image as the differential image.

Here, the differential filter may include a sobel filter, a prewitt filter, a Roberts filter, a LoGlac filter of the Laglacian filter.

As described above, the smoothing filtering device may measure a gradient value by differentiating the smoothing image in the X and Y-axis directions, respectively. Through this, the smoothing filtering device may provide an environment capable of effectively removing noise while maintaining edge contrast of anatomical feature points of body organs based on the measured gradient values.

Next, in operation S430, the smoothing filtering apparatus may filter the differential image by using a reference value for the generated differential image. That is, the smoothing filtering device may filter the first differential image by using a first reference value with respect to the first differential image. In addition, the smoothing filtering device may filter the second differential image by using a second reference value for the second differential image.

To this end, the smoothing filtering device may threshold the first differential image to calculate a first threshold value of the first differential image, and set the calculated first threshold value as the first reference value. have. In addition, the smoothing filtering device may threshold the second differential image to calculate a second threshold of the second differential image, and set the calculated second threshold as the second reference value. .

Accordingly, the smoothing filtering device may filter a first differential image corresponding to a value less than or equal to the first threshold value among values of the first differential image, and among the values of the second differential image, the second threshold value. The second differential image corresponding to a value below the value may be filtered.

Alternatively, the smoothing filtering device obtains a first average value and a first variance value for the first differential image, and adds the first average value and the first variance value (first average value + first variance value) to obtain a result. A first total value is calculated, and the calculated first total value can be set as the first reference value. In addition, the smoothing filtering device obtains a second average value and a second variance value for the second differential image, and adds the second average value and the second variance value (a second average value + a second variance value) as a result. 2 total values are computed, and the calculated 2nd total value can be set as said 2nd reference value.

In this case, the smoothing filtering device may obtain the first average value and the second dispersion value within a window having a predetermined size centering on each pixel in the first differential image. In addition, the calculation unit 130 may obtain the second average value and the second variance value within a window of a predetermined size centering on each pixel in the second differential image.

Accordingly, the smoothing filtering device may filter the first differential image corresponding to a value less than or equal to the first sum value among the values of the first differential image, and the second sum among the values of the second differential image. The second differential image corresponding to a value below the value may be filtered.

Next, in operation S440, the smoothing filtering apparatus may integrate the filtered first differential image and the filtered second differential image, respectively. In this case, the smoothing filtering device may integrate the filtered first derivative image and the filtered second differential image, respectively, by using a reverse filter.

Here, the reverse filter may be implemented to make the differential filter 1, that is, delta.

Embodiments of the present invention include computer readable media including program instructions for performing various computer implemented operations. The computer readable medium may include program instructions, local data files, local data structures, or the like, alone or in combination. The media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical recording media such as CD-ROMs, DVDs, magnetic-optical media such as floppy disks, and ROM, RAM, flash memory, and the like. Hardware devices specifically configured to store and execute the same program instructions are included. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

While specific embodiments of the present invention have been described so far, various modifications are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined not only by the claims below, but also by the equivalents of the claims.

As described above, the present invention has been described by way of limited embodiments and drawings, but the present invention is not limited to the above-described embodiments, which can be variously modified and modified by those skilled in the art to which the present invention pertains. Modifications are possible. Accordingly, the spirit of the present invention should be understood only by the claims set forth below, and all equivalent or equivalent modifications thereof will belong to the scope of the present invention.

1 is a block diagram illustrating a smoothing filtering apparatus according to an embodiment of the present invention.

2 is a diagram illustrating an example of an original image before embodiments of the present invention are applied.

3 is a diagram illustrating an example of an image after embodiments of the present invention are applied.

4 is a flowchart illustrating a smoothing filtering method according to an embodiment of the present invention.

<Description of the symbols for the main parts of the drawings>

100: smoothing filtering device

110: smoothing part

120: differential

130: calculation unit

140: filter unit

150: integral

160: control unit

Claims (15)

A smoothing unit which generates a smoothing image by smoothing the original image; A derivative unit for generating a differential image by differentiating the smoothing image; A filter unit for filtering the differential image using a reference value for the differential image; And Integrator that integrates the filtered differential image Smoothing filtering device comprising a. The method of claim 1, A calculation unit configured to calculate a threshold by thresholding the differential image More, The derivative part measures a gradient value for the differential image, And the filter unit sets the threshold as the reference value, and filters the differential image measured by the gradient value less than or equal to the threshold value among the gradient values. The method of claim 1, A calculation unit for calculating a sum value obtained by calculating an average value and a variance value of the differential image More, The derivative part measures a gradient value for the differential image, And the filter unit sets the total value as the reference value and filters the differential image measured by the gradient value equal to or less than the total value among the gradient values. The method of claim 1, A calculation unit calculating a sum value obtained by calculating an average value and a variance value of the differential image in a window having a predetermined size centering on each pixel in the generated differential image. Smoothing filtering device further comprising. delete The method of claim 1, The smoothing unit, And a smoothing filter applied to the original image to smooth the original image. The method of claim 1, The derivative part, Smoothing filtering device for generating the differential image by differentiating the smoothing image in the X-axis direction and the Y-axis direction, respectively. The method of claim 1, The derivative part, A smoothing filtering device for differentiating the smoothing image by applying a differential filter. The method of claim 1, The integral part, A smoothing filtering device for integrating the filtered differential image using a reverse filter. Smoothing the original image to generate a smoothing image; Generating a differential image by differentiating the smoothing image; Filtering the differential image using a reference value for the differential image; And Integrating the filtered differential image Smoothing filtering method comprising a. The method of claim 10, Calculating a threshold by thresholding the differential image More, Generating the differential image, Measuring a gradient value for the differential image, Filtering the differential image, Setting the threshold as the reference value; And Filtering differential images measured from gradient values below the threshold value among the gradient values Smoothing filtering method comprising a. The method of claim 10, Calculating a sum value obtained by calculating an average value and a variance value of the differential image More, Generating the differential image, Measuring a gradient value for the differential image, Filtering the differential image, Setting the total value as the reference value; And Filtering differential images of the gradient values measured with gradient values less than or equal to the sum value Smoothing filtering method comprising a. The method of claim 10, Calculating a sum value obtained by calculating an average value and a variance value of the differential image in a window of a predetermined size centering on each pixel in the generated differential image; Smoothing filtering method comprising more. The method of claim 10, Generating the smoothing image, Smoothing the original image by applying a smoothing filter to the original image Smoothing filtering method comprising a. The method of claim 10, Generating the differential image, Generating the differential image by differentiating the smoothing image in an X-axis direction and a Y-axis direction, respectively Smoothing filtering method comprising a.

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