Prostate cancer is one of the leading causes of cancer death in men. Early detection of prostate cancer is very essential for the success of the treatment.
PROSTATE'S BOUNDARY DETECTION IN TRANSRECTAL ULTRASOUND IMAGES USING SCANNING TECHNIQUE Joseph Awad, T. K. Abdel-Galil, M. M. A. Salama, A. Fenster, ,K. Rizkalla and D. B. Downey H. Tizhoosh University of Waterloo
University of Westem Ontario
Abstract Prostate cancer is one of the leading causes of cancer death in men. Early detection of prostate cancer is very essential for the success of the treatment. Ultrasound B-mode images is the standard mean f o r imaging the prostate. The manual analysis of the ultrasound images consumes much time and effort. It is necessary to develop an automated algorithm to analyze the ultrasound images. Thefirst important step in detecting the cancer is to detect the boundary of the prostate itself and to extract it from the imagefor further analysis. In this paper a multi-stage algorithm forprostate boundary detection is proposed. In the first stage, the proposed algorithm starts with enhancing the contrast of the image by sticks technique followed by smoothing the image by gauss kernel. In the second stage, scanning the image and applying knowledge base rules to find a seed point inside the prostate is implemented. This seedpoint is used to remove the false edges. Then by using a morphological opening algorithm, the remaining false edges can be removed. The final step is to use the seed point to scan the image in radial directions tofind the prostate ' s boundary.
wavelets. Other researches work with thresbolding, region growing, classifiers, clustering, Markov random field models, Artificial Neural Networks, and atlas-guided approaches [2]. Expert can detect the boundary of the prostate very well in the TransRectal Ultrasound (TRUS) images by the naked eyes. Examining a large set of these images taking in a preset scanning angle can therefore determine the volume of the prostate. This volume is a valuable information in diagnosing prostate cancer. This process is time consuming. The purpose of the present research is to aid the expert in speeding up this process by precondition the images for his examination. This will be achieved by developing an algorithm that will segment the prostate more rapidly and at the same time retain accurate results. The outline of the paper is as follows the proposed segmentation algorithm is introduced in Section 2. In Section 3, Validation of the proposed algorithm is presented using various Transrectal Ultrasound images. The conclusion ofthis research is given in Section 4.
Keywords: Edge detection, image segmentation, prostrate cancer.
1. INTRODUCTION There is no standard segmentation technique that works in all images. Each type of medical images needs special treatment. Many researches studied the segmentation of the images using different techniques. Some researchers use deformable contour. In the early 1970s, the idea of using an optimization method to find the object boundary in images emerged, hut had not become popular until Kass et. AI. presented his remarkable research [I]. Many researchers followed Kass and used his active contows algorithm as basses for several researches in this area. This algorithm was further enhanced by combining it with other techniques such as Neural Networks, Fuzzy, and
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2. ALGORITHM DESCRIPTION Research paper [3] introduced an algorithm of prostate segmentation, however, the seed point should be identified manually and the algorithm has no build in automated capabilities to reject any false edges. , To overcome these limitations, a multi-stage segmentation algorithm is proposed in this paper to automatically identify the seed point inside the prostate and to remove more false edges by applying morphological opening. The algorithm used in this research is summarized in
Figure.1.
2.1 Contrast Enhancement Boundary detection in an ultrasound images is very challenging problem because of the presence of speckle noise. In medical ultrasonic, it is believed that the speckle conveys information about the region being imaged, although the exact method of interpreting that information is in dispute [4]. To eliminate these speckles that corrupt the images and at the same time enhance the edges, the sticks technique is utilized.
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Sticks Technique For N
N neighbourhood in the image, there are 2N 2 short lines that pass through the central pixel, with N pixels in length. The sum of pixel values along each line segment is calculated. The largest sum of segments is put in center pixel of N N sub matrix in the image. This step is repeated for all the images [3]. This process increases the intensity level of image. Hence, the next step is to readjust the intensity level of the image to original level. In our algorithm, the sticks technique is repeated with different lengths kom 3 pixels to 17 pixels with increment 2 to enhance also the prostate edges in sharp curvature parts. Figure Pshows different sticks with five pixels in length [ 5 ] . ~
Read Image
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U s i n q Sticks Technique
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Edge Detection By Canny
Contrast Enhancement
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Fig. 2. Different sticks with five pixels in length
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I Apply
Knowledge Base Rules
I Morphological Opening Algorithm
I Scaning the Image in Radial Directions Fig. I . Prostrate Segmentation Flow Chart
2.2. Image Smoothing After contrast enhancement using the sticks technique, it is important to smooth the image to be ready for edge detection using Canny technique. Hence, we apply Gauss kernel for this purpose with variance equal to 5.
This method will speed up the analysis of images. To enable the computer to detect a seed point inside the prostate, we should first to employ our knowledge about the prostate. We know that the intensity level of the prostate is low with respect to its surrounding area. We know also, that the prostate is not in perimeter of the image but not necessarily in the medial. Hence, WI: are looking for the low intensities pixels inside the imagi: and not in the perimeter, noticing that the background of the image is black and this may affect the result of the algorithm It is important therefore to replace the background of the image by white color before searching for the low intensities pixels. The algorithm basically sorts the pixels inside the image according to their intensities and then picks the 20% of the low intensities pixels and determines the mean of coordinates of these pixels to determine the coordinate of seed point.
2.4. Canny Edge Detection
23. Seed Point Localization It is very desirable to make the program fully automated. By this way, it will not wait for the user response. It can work as a stand alone for twenty-four hours a day and dealing with a numerous number of images.
After smoothing the image we need now to get the edge map. The Canny technique was found suitable for this stage. It avoids the loss of the weak edges and the false edges can he removed latter by knowledge base and morphological opening techniques.
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Fig. 3 (a) The original image (b) Contrast Enhancement (c) Edge Detection by Canny (d) Knowlodge Base and Morphological Opening (e) Prostate Boundaty Superimposed on the h a g
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2.5. Application of Knowledge Base Rules Human sight is very complex system and it employs gained knowledge to recognize the picture. The expert knowledge enables the expert to retrieve more information from the image compared to the normal person. In this paper we will build a knowledge base rules about the image that will be imbedded in the algorithm and will help in successhlly segment the image. Some of these rules are discussed in section 2.3.
2.6. Morphological Opening After applying the knowledge base rules for the edge map and removing most of the false edges, there are still some false edges most of them has a short length. To remove these false edges, we applied the Morphological opening technique, by which, the short linked pixels with areas less than 50 pixels can be eliminated.
transducer into the rectum as for the conventional TRUS exam. The 3-D scan is then initiated, causing the motor to rotate the transducer along its long axis, while 2-D images are being digitized and stored. In the current system, for a side-fuing probe, about 150 images are digitized in about 5 seconds, with a rotation of 100’. The proposed segmentation algorithm is applied for different frames for 5 persons (using the system described above) and the algorithm detected the boundary of the prostate successively. Matlab 6.1 is used to implement the algorithm. The program takes 5 min, running on 1.3 GHz computer, to automatically detect the boundary OF the prostate. Figure 3, shows the result of the program for three different prostate orientations. Images (a) is the original image, @) is the result after applying isticks technique with stick length from 3 to 17 with incremmt 2, (c) is the edge map which results after applying Canny edge detection on the smoothed image. (d) is the edge map which result after applying knowledge base and morphological opining with 50 pixels in area. (e) is the prostate boundary superimposed on the original image.
2.7. Boundary Detection by Radial Scanning
4. CONCLUSIONS
The h a 1 step is to detect the boundary of the prostate. This can be done by scanning the edge map in radial directions from the seed point. Keeping in mind that there might he still some false edges in the edge map, and it is important to filter these edges employing our knowledge about the perimeter of the prostate. It is known that the prostate has a smooth curvature shape. This algorithm analyses the obtained edge and filter out any pixels that may violate smooth curvature characteristics. The final step in his algorithm is to use these edge pixels and interpolate the known edge pixels to find the missing parts of the contour and consequently obtain the best spline that fits these pixels. The result of this stage is to construct a complete smooth prostate edge witbout the need to use the deformable contour method.
This paper proposes a multi-stage computerized method to automatically detect the boundary of h e prostate in TRUS images. Results, presented in Section 3, show that the proposed algorithm can extract efficiently the prostate ftom the image for further analysis.
References [ I ] M. Kass, A. Witkin, and D.Terzopoulos, “ Snakes: Active Contour Models ,” Inf.J. Compuf.Vis.,vol.1,pp. 32 1-33 1,1987. [2] D. L. Pham, C. U, and L. Prince, “Current methids in medical image segmentation,” Annual Rm’w of Biomdical Engineering, vol. 2 pp. 315-337,2000. [3] S. D. Path& V. Chalana, D. R. Haynor, Y. Kim, “Edge Guided Boundary Delineation in Prostate ultrasound Images,” IEEE Trans. on Medical Imaging, vol. 19, no. 12,
December 2000.
3. RESULTS
[4] R. N. Czenuinski, D.L. Jones, and William D. 0 Brim, Jr., “ Line and Boundary Detection in Speckle Images,” IEEE
The system that OUT team members at UWO have developed consists of a PC with a video frame grabber used to capture the Bmode ultrasound images at 30 frames/sec. A custom built motorized probe mounting assembly, developed in UWO lab, is interfaced to the microcomputer and is controlled by software developed in UWO lab. The probe assembly is composed of two parts one is common to all ultrasound transducers and contains the motor and the mntroller interface, while the second is designed to fit each manufacturer s specific transducer (both side-firing and end firing). This system is designed to obtain 3-D image by mounting the conventional transducer into the probe assembly and then inserts the
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Trans. on Image Processing, vol. I,no. 12, December 1998. [SI R. N.CzenuinSki, D.L. Jones, and William D. 0 Brien, Jr, “ Detection of Lines and Boundaries in Speckle higes Application to Medical Ultrasound,” IEEE Transactions on Medical Imaging, vol. 18, no. 2, February 1999.
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