Speckle Noise Reduction and Segmentation of Kidney Regions From Ultrasound Image Tanzila Rahman, Mohammad Shorif Uddin Dept. of Computer Science and Engineering Jahangirnagar University Dhaka, Bangladesh
[email protected] Abstract— Ultrasound imaging plays a crucial roles in medical field to estimate kidney size, position, appearance and helps to detect structural abnormalities as well as the presence of cysts, stones, cancer, congenital anomalies, swelling, blockage of urine flow etc. But presence of speckle noise and low contrast in ultrasound images, detection of kidney is a difficult as well as challenging task. In this paper we develop and implement a system, which can segment human kidney from ultrasound images, usable during surgical operations like punctures. First, we take input image and perform restoration on that image. Then we reduce speckle noise and smooth resultant image using Gabor filter. Histogram equalization is used to enhance the image quality. For this study, two segmentation techniques were chosen to be compared consist of cell segmentation and region based segmentation. For better result we use region based segmentation to extract kidney regions. Lastly we perform refinement and crop the segmented kidney region from the original image. Keywords-Ultrasound image; Speckel noise; Image restoration; Segmentation; Gabor filter.
I.
INTRODUCTION
Kidneys are retroperitoneal organs, located near the middle of the back, just below the rib cage, one on each side of the spine. Every year in both developed and developing countries, many people affected by chronic kidney failure due to diabetes mellitus and hypertension, glomerulonephritis etc. Worldwide research indicates that one out of 10 adults had kidney problems and by 2015 it is estimated that about 36 premature deaths due to kidney disease will happen [2]. Since kidney function impairment can be life threatening, diagnosis of the disorders and diseases in the early stages is crucial. Ultrasound is one of the non-invasive low cost widely used imaging techniques for diagnosing kidney diseases. Though ultrasound image is adaptable, transferable and comparatively safe, but this type of image often full of acoustic interferences (speckle noise) and artifacts. Speckle is a complex phenomenon, which degrades delectability of target organ and reduces the contrast, resolutions with back-scattered wave appearance which originates from many microscopic diffused reflections. It affects the human ability to identify normal and pathological tissue. Hence, the automatic segmentation of anatomical structures like kidney in ultrasound imagery is a real challenge.
Automatic segmentation of kidney region is necessary due to the following reasons: Segmentation is typically used to delineate the boundaries between different tissues in order to make a size measurement or to characterize the difference between healthy versus diseased. Some organs lie close to the kidney may give effect to the performance of other image processing [1], finding kidney region may helpful to improve the speed and accuracy of further segmentation process. Kidney segmentation method also usable during surgical operations such as punctures. It is one of the key system for developing computeraided diagnosis systems for percutaneous renal intervention. This system can help to find some significant parameters e.g. M-mean, M-med, M-mod, M-max and M-min. These parameters are used to characterize kidney disorders, which helps in classification and identification of kidney disorders with ultrasound scan. Hence our main objective is to segment kidney region from ultrasound image. We described a system for segmenting kidney regions from ultrasound images. This paper is organized as follows: section 2 provides a description of the principle of the proposed technique. Our proposed method is discussed in section 3. In section 4 experimental results with ultrasound images are discussed. Finally, the conclusions are drawn in section 5. II.
PRINCIPLE OF PROPOSED TECHNIQUE
In medical field, Ultrasound imaging plays an important role for early detection of any kidney disorders and diseases which could reduce end-stage renal disease (ESRD). Moreover, it is economical, comparatively safe, transferable, and adaptable. Thus, ultrasound imaging is used for different purpose in clinical field. But due to the presence of speckle noise detecting the kidney is much difficult and depends much on the sonographer. Hence, here we describe and implement a system that can reduce speckle noise and segment kidney regions from ultrasound scans. A typical block diagram of our proposed method is given in Fig.1.
Sample Ultrasound Images
Image Restoration
Smoothing and sharpening using Gabor filter
Contrast enhancement using histogram equalization Pre-Processing
Use Segmentation to find kidney regions
Refinement and Crop Candidate Regions from Original Image Candidate region extraction Figure 1. Block diagram of kidney segmentation system. III.
METHODOLOGY
Because of speckle, shadows, signal dropout and low contrast segmentation of kidney from ultrasound image become a challenging task. Hence, the aim of this study is to develop a system that can segment kidney region from ultrasound. The whole process is divided into two distinct stages: i. ii.
Pre-processing. Candidate region extraction.
A. Pre-processing For region based method good quality image is essential. For this, in pre-processing we deal with speckle noise and low contrast. This is divided into three steps which are described according to their order of use in our system. 1. 2. 3.
Image restoration Smoothing and sharpening using gabor filter. Contrast enhancement using histogram equalization.
1) Image restoration: The aim of image restoration is to remove or reduce the degradations that have occurred while the digital image was being obtained. In Our system we use level set function for proper orientation. In plain curvature motion,
the curve smoothes and shrinks eventually disappears. Thus, Merriman and Sethian proposed evolution between max(,0) and min(,0). max( , 0 ) F min( , 0 )
if a ( x , y ) G ( x , y ) otherwise
Where, a(x,y): average intensity-small neighborhood. G(x,y): median in the same neighborhood. 2) Smoothing and sharpening using gabor filter: Gabor filter, originally introduced by Dennis Gabor, is used to obtain the optimal resolution in both spatial and frequency domains by acting as a band-pass filter for the local spatial frequency distribution [3]. It is widely used as convolution operator to smooth images, noise removal, edge detection and also for segmentation of texture features especially in the fingerprint recognition. By varying the standard deviation of the Gaussian function, the degree of smoothing can be adjusted [3]. The equation for 2D Gaussian function is given below: ge(x,y)= go(x,y)= Where, = The centre frequency. = Spread of the Gaussian window. 3) Contrast enhancement using histogram equalization: Histogram equalization is one of the most significant part of image analysis. It improves contrast and the goal of histogram equalization is to obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image. It redistributes intensity distributions. In our system, we enhance the contrast of images by transforming the values in an intensity image, or the values in the colormap of an indexed image, so that the histogram of the output image approximately matches a specified histogram. If the data type of input to the I port is floating point, the input to hist port must be the same data type. The output signal has the same data type as the input signal. B. Candidate region extraction Using all the steps described above, we minimize the speckle noise, smooth and enhance ultrasound image quality. Now we need to extract candidate regions. This phase can be divided into two steps. 1. Segmentation. 2. Refinement and cropping. 1) Segmentation: Once the original image filtered to correct the artifacts, segmentation method is used to segment
the kidney region. For this study, two segmentation techniques were chosen to be compared consist of cell segmentation and region based segmentation. From the visual inspection we see that region based segmentation gives better result that cell segmentation. i) Region Based Segmentation: It is a simple and pixelbased image segmentation method since it use initial seed point. These method examines neighboring pixels of initial ―seed points‖ and determines whether the pixel neighbors should be added to the region[ 13]. The basic formula for Region-Based Segmentation is given below: Let R represent the entire image region. The segmentation process partitions R into n subregions, R1, R2, . . . , R n, such that. 1. . 2. Ri is a connected region, i= 1,2,...,n. 3. Ri Rj= , for all i and j. 4. P(Ri)=TRUE, for i=1,2,..... 5. P(Ri Rj)=FALSE. (ii) Cell Segmentation : Basic formulation for cell segmentation is given below [15]. 1. Convert input image as 2D image. 2. Contrast adjustment isn't usually necessary for segmentation, but it can help the algorithm developer see and understand the image data better. 3. Convert into binary image. 4. Clean that up and then overlay the perimeter on the original image. 5. Find a way to "mark" at least a partial group of connected pixels inside each object to be segmented. 6. Clean that up and then overlay it.
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2) Refinement and cropping : With our candidate kidney region there exists some small objects and the background. We can extract the background by removing the largest object using connected component analysis and morphological opening. There are several operators in MATLAB Image Processing Toolbox such as bwareaopen, imopen, imclose, and imfill. bwareopen morphologically open a binary image and remove small object and it is usually used to remove background. Lastly, using bounding box, we estimate the desire area and crop the kidney area from the original input image. IV.
EXPERIMENTAL RESULTS
To evaluate the performance of our method for segmenting kidney from ultrasound images, we used 10 images in our experiment. All the images were taken from web. Fig.2 shows some sample images of ultrasound scan. Using pre-processing we reduce speckle noise and enhance image quality which will helpful for extraction processing. The results of pre-processing are shown in fig.3. In fig.4 we showed the result of two segmentation process, region based and cell segmentation. From visual inspection we see that region based segmentation gives better result by identifying kidney regions where cell segmentation identifies cells only. Thus we use region based segmentation in our experiment for segmenting kidney region from ultrasound scan. The resultant image found from region based segmentation contains background with smaller objects. As background is the largest region among all objects, we can remove it as maximum region and get only candidate region with smaller objects. These smaller objects can be removed using connected component analysis. Finally we crop candidate kidney region from original image.
(b) Figure 2. Input ultrasound images.
(c)
image after gabor filter
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(c) (b) Figure 3. Output images (shown in Fig.2) after pre-processing with less speckle.
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(b) (c) Figure 4. Output images (shown in Fig.2) after region based segmentation.
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Figure 5. Images (shown in Fig.2) after cell segmentation.
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Figure 6. Candidate kidney region (shown in Fig.2). V.
CONCLUSION
A method of segmenting kidney regions from ultrasound image is discussed. It can’t only detect kidney region, but also remove speckle noise and enhance image quality. The whole process is divided into two distinct stages : Pre-processing and Candidate region extraction. Pre-processing deals speckle noise and artifacts with low contrast image. After removing noise, kidney region is extracted using proper segmentation method. In our study we also compare region based segmentation with cell segmentation. As region based segmentation gives better result we use this type of method in our experiment. The experimental results appear encouraging to demonstrate the efficiency of the system. The segmenting regions can be used further for many clinical operations and image analysis. We hope our method is useful for segmenting kidney from ultrasound image. Our future concentration will
be on the removal of speckle noise and artifacts with segmenting kidney from those images where kidney boundary are not much clear. ACKNOWLEDGMENT Authors were supported by the Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh. REFERENCES [1]
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