SEGMENTATION OF PROSTATE BOUNDARIES USING ... - IEEE Xplore

SEGMENTATION OF PROSTATE BOUNDARIES USING REGIONAL CONTRAST ENHANCEMENT Farhang Sahba, Hamid.R Tizhoosh, Magdy M.A. Salama Departments of Systems Design Engineering, Electrical and Computer Engineering University of Waterloo Waterloo, Ontario, Canada ABSTRACT In this paper a novel method for automatic prostate segmentation in transrectal ultrasound images is presented. Morphological grey level transformations are first used to generate an image with enough bright intensity around the prostate. This image is then thresholded to produce a binary image. Then by finding and using a point as the inside point for the prostate, a Kalman estimator is used to isolate the prostate boundary from any irrelevant parts and produce a roughly segmented version (as coarse estimation). Consequently, a fuzzy inference system describing regional and gray level information is employed to enhance the contrast of the prostate with respect to the background . Using strong edges obtained from this enhanced image and information from pixels gradients and also the characteristics in the vicinity of the coarse estimation, the final boundary is extracted. A number of experiments are conducted to validate this method. 1. INTRODUCTION Prostate cancer is the most frequently diagnosed cancer in men and ultrasound imaging is a widely used technology for diagnosing this kind of cancer [1]. The accurate detection of prostate boundary in ultrasound images is very important for automatic cancer diagnosis/ classification. However, in these images, the contrast is usually low and the boundaries between the prostate and background are fuzzy. Some other methods have been introduced to investigate automatic or semi-automatic segmentation of the prostate boundaries from the ultrasound images [2],[3] . In this paper, we will present a new region- and grayscale-based approach for prostate segmentation in ultrasound images. It consists of several sequential stages to produce the final segmentation. However, because of space restrictions, we can not discuss the detail of structure here.In order to implement this method we have made these assumptions: 1-The prostate has a walnut-shape . 2-The intensity of the prostate boundary in the ultrasound images has a dark to light transition from the inside of the prostate to the outside of that.

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2. ALGORITHM DESIGN The proposed algorithm contains four main stages. First, after smoothing, using grayscale morphological transformation, called Top-hat and bottom-hat filtering, a primary version of the image which has enough bright intensity around the prostate is produced. Second, the obtained image is thresholded and then eroded for several times until an isolated object related to the prostate is produced. Central point for this object can be considered as an inside point (around the central area) of the prostate. Subsequently, using a Kalman estimator , a coarse estimation for prostate boundary is obtained. This coarse estimation is not accurate, but we just use it as the input data for the next stages. Third, using coarse estimation, a regional fuzzy membership function is established. By defining a fuzzy inference system, a contrast enhancement can be obtained exactly in the area of the prostate. Finally, the algorithm finds potential boundaries and extracts the final segmentation using the information about gradient and vicinity to the coarse estimation boundary. 2.1. Finding the Central Point and Coarse Estimation a) Smoothed Version: TRUS images are heavily corrupted with the noise. It will be discussed that we need just a rough estimation for the next stage, so we can slightly remove the noise using a simple median and/or an average filter. Combination of these filters can slightly smooth noise and deliver a smoothed texture for the inside and the outside of the prostate. It is very important to be noticed that this smoothed version is used only for the ”next step”. For the final segmentation we use the ”original” ultrasound image. b) Morphological Operators and Exaggerated Bright Gaps: To enhance the edges around the prostate boundaries, two morphological filters namely Top-hat and Bottom-hat filters have been used [4]. For our case, considering the shape of the prostate boundaries and bright gaps around them and also the fact that we need a structuring element which has enough effect on gaps with different shapes, a small disk

structuring element (for instance containing 8 pixels) is good. In this stage our goal is to maximize the contrast of the bright gaps around the prostate. A useful technique is the combined use of top-hat and bottom-hat transformations [4]. Combination of these transformations tend to isolate grayvalue objects that are bright on a dark background. Because there are dark textures inside of the prostate and brighter ones outside of that, the transformations can highlight the bright gaps around the prostate. In order to maximize the contrast between the whole prostate and the bright gaps around it, we can add the top-hat transformation of the image to the original image, and then subtract the bottom-hat transformation from the result. Fig. 1(a) and 1(b) show the original image and the smoothed version and Fig. 1(c) shows the result of applying two morphological filters on the smoothed image. Using a global thresholding the output is a binary image. The main advantage of this method is that the large and bright gaps produced around the prostate have distinctly large gray level intensities , and for a large category of ultrasound images, a good performance of global thresholding can be expected. c) Inside Point and Extracting a Coarse Estimation: First, the holes inside the thresholded image must be filled. It is shown in Fig. 1(d) and complemented for simplicity. It is done using the standard region filling morphological algorithms that fill the holes having the area less than a particular value [4]. Now an erosion operator must be applied on the filled image for several times (say ke )until an isolated object (related to the prostate) in the central area of the image remain. Considering the geometrical location and/or its area, the separated object obtained from the prostate is easily distinguished. Then the central point inside of this object can be obtained . Because the object is produced by ero-

Fig. 1. (a) Typical ultrasound image of the prostate. (b) Smoothed image. (c) The result of grayscale morphological filters. (d) Thresholded image. sion in all directions, this point (O) is located somewhere around the real central area of the thresholded prostate . Now to find a coarse estimation for the thresholded prostate we can use some properties of Kalman estimator to extract

the valid data from primary thresholded image. Using the inside point O, the state vector x = [ r r˙ θ θ˙ ] is considered, where r is the distance between the point O(xc , yc ) and the pixel (xp , yp ) located on the edge of the coarse estimation and θ is the angle between the vertical axis and r. Hence, the following equations can be considered for r ,θ: 1

r = [(xp − xc )2 + (yp − yc )2 ] 2 θ = tan−1

yp − yc xp − xc

(1) (2)

y˙ cos θ − x˙ sin θ θ˙ = r

(3)

r˙ = y˙ sin θ + x˙ cos θ

(4)

where r˙ and θ˙ are the radial and angular velocity, respectively. For calculation of the coarse version, a Kalman estimator can be used. It receives its first data from a point placed on the vertical axis and located around the border of thresholded prostate. For simplicity, in each sequential iteration, we can use the average point of m (= 1 to 10) pixels as measured data along the border of thresholded prostate. The predicted values determine the next pixel on the border by considering the variation on r and θ. A gate, the so-called ”association gate”, is considered around the predicted pixel. Only the pixels located on the border of thresholded prostate and inside of this gate are considered as valid data for the estimator. To use the correct data around the thresholded prostate, we may also use the expert knowledge as predefined template for the prostate. In some cases a straightforward way to find the template is that if we have done ke times erosion on the image to find the isolated object , we can do kd = ke times dilation on the isolated object to find a suitable-shape template . For better result we can also implement a pre-categorization algorithm for the pixels to extract the more valid data for the filter. Using such an algorithm, those pixels that have a specific degree of similarity with respect to their neighbors, are the valid data for the estimator. In the case, when there are more than one valid pixel, the nearest one with respect to the predicted pixel can be taken into consideration. The updated values for r˙ and θ˙ show the changes for the r and θ on the coarse boundary that is being estimated. For the case that there is no data inside the gate, the predicted value must be considered as the data. Also in all cases, the association gate must change its location so that follows the boundary . Employing this method, the irrelevant data (like shadow) on the boundary can be omitted effectively. For an effective performance, the size of association gate must be varied adaptively based on the covariance of the Kalman estimator. The gate must maximize the presence of valid data and minimize invalid

ones. To find such a gate the following function must be maximized [6]: P1 (k) L(k) = (5) P0 (k) where P1 (k) and P0 (k) are the probabilities of presence of valid data and invalid data in the gate, respectively. This leads us to check the value of a statistical distance d with respect to a value T so that d2 ≤ T , where d2 = lT P −1 l and l is the geometrical distance and P is the covariance matrix in the Kalman estimator. In two dimensional problems l = (l1 , l2 ) and it is changed to al12 + (b + c)l1 l2 + el22 ≤ T . a, b, c and e are dependent to the values of covariance matrix. The value of T is generally a constant depending on the features of the problem. Using above approaches the system is able to follow prostate boundary variations and isolate its thresholded version from connected irrelevant parts. Then a coarse estimation can be extracted. This coarse version is just used as a primary contour to produce the data needed for the next stage. 2.2. Contrast Enhancement A regional approach is used on the original ultrasound image to increase the prostate contrast with respect to the background. This amplifies the strength of the outer prostate edges. Using the coarse estimation, two contours (as inside and outside contours) can be obtained such that the true boundary is ideally located between them almost in all regions. For extracting the inside contour, erosion operator with a symmetric structuring element can be employed. With the same rule and using dilation operator, the outside contour can be obtained as well. The configuration of erosion and dilation is usually the same for most US images. These two contours are employed as two levels to define a membership function. This membership function determines to what degree does a pixel belong to the prostate. Fig. 2(a) illustrates the membership function based on the pixel position. In this function position is determined based on relative distance from the inside contour. In order to use this function , the nearest distance of the pixel from the inside contour must be determined. For establishment of a fuzzy inference system (FIS) for contrast enhancement, the rules can be defined as follows: If the pixel does not belong to the prostate then leave it unchanged. If the pixel belongs to the prostate and is dark then make it darker. If the pixel belongs to the prostate and is gray then make it dark. If the pixel belongs to the prostate and is bright then make it brighter. The membership function for input gray levels is shown in

Fig.2(b). For the output membership function, fuzzy inference system uses singleton functions with some values like [7]: S1 =1 ; S1 =64 ; S1 =255

Fig. 2. (a) A simple representation for location membership function.(b) A simple representation for input gray level membership function . (c) Enhanced Image using proposed fuzzy inference system.(d) Automatic and manual boundaries shown on the original images. Solid line is the result of manually segmented image and dash line is the result of proposed algorithm. Using these rules we enhance the contrast of the image not only based on the gray level values but also on the location of each pixel . The result of our proposed FIS is shown in Fig. 2(c). It clearly demonstrates that the result is an image with higher contrast in the prostate area. 2.3. Final Segmentation In previous section we achieved an image with selectively high-contrasted prostate area and almost no changes in other regions. So we have strong edges in the boundaries of the prostate. Canny edge detector is used to detect the edges located between inside and outside contours [8]. The potential boundary pieces obtained by detected edges may contain some branches. Among these branches those with higher likelihood for being a true piece, are the ones we are looking for. Because in preceding section, we enhanced the contrast of the prostate area, the pixels which have gradient value less than a specific value Gmin can be specified and removed. A straightforward and useful way for the gaps between very adjacent pieces is to fill those gaps directly by straight lines. The criterion for connectivity for the procedure is 8-connectedness. Now there are some potential pieces of boundaries and the final boundaries must be extracted from them. Among these pieces, those with maximum gradient and minimum distance with respect to the

boundary of the coarse estimation could be taken to consideration. We must calculate these criteria around the coarse estimation. When each piece is completed, we go to the next one and pursues the procedure again until a complete outline for the whole prostate is achieved. If there exist N pixels on an edge, the following equations could be used as two criteria for edge selection: N 1 X Gi N i=1

(6)

N 1 X 2 d N i=1 i

(7)

where Gi is the gradient of ith pixel and di is the distance of that pixel with respect to the boundary of the coarse estimation. Some other factors like the angle of edge pieces with respect to the angle of coarse estimation may used for edge selection as well. The result of employing this method and also the outline obtained manually by radiologist are shown in Fig. 2(d). They are shown on the original image.

3. RESULTS AND DISCUSSIONS We selected nineteen TRUS images to examine the method. Most of the selected images were contaminated with noise and shadow. The results of our proposed method have been evaluated by comparing the algorithm-based segmentations and the manual segmentations as ”gold standard” on these images. From the experiments, it can be seen that the proposed method is robust and able to deliver good results. Visually, the difference between two contours is not considerable. Moreover, we have used the average distances and the area errors to show the performance of the algorithmbased segmentations compared to the manual segmentations for those images [9]. Table I summarizes the results. Compared to the manual raters , our experimental results are very promising and show that the proposed method can segment the prostate boundary in selected ultrasound images accurately. Table 1. Quantitatively Evaluation of Proposed Algorithm in Comparison with Manual Segmentation as ”gold standard”. Average Dist. (Pixels) Area Err. (%) Mean 3.3 2.4 Var 1.3 1.1

4. CONCLUSION A new regional fuzzy contrast enhancement model for segmentation of the prostates in TRUS images has been presented in this paper . In comparison with manual segmented images, the experimental results are very promising and show that our approach can segment the prostate boundary for these TRUS images efficiently and accurately. For further work this methodology can be continued in the following aspects: More samples containing the manual prostate segmentation by the radiologists are required in order to verify the segmentation accuracy more reliably. Additionally, by using an adaptive approach for speckle noise reduction, we can improve the quality of the image. This can give us better results in the next stages. Finally, as most important thing, we should integrate the radiological expert knowledge into the most parts of the proposed algorithm as much as possible. 5. REFERENCES [1] Cancer Facts and Figures. American Cancer Society. [Online] http://www.cancer.org, 2002. [2] H. M. Ladak, F. Mao, Y. Wang, D. B. Downey, D. A. Steinman, A. Fenster, “Prostate boundary segmentation from 2D ultrasound images,” Med. Phys., 27 , pp. 17771788,, 2000. [3] S. D. Pathak, V. Chalana, D. R. Haynor, and Y. Kim, “Edge guided boundary delineation in prostate ultrasound images,” IEEE Trans. Med. Imag., 2000. [4] Gonzaless,R.C. Woods, R.E., “Digital image processing” Addison- Wesley, 2001. [5] Y. Choi, R. Krishnapuram, “A Robust Approach to Image Enhancement Based on Fuzzy Logic,” IEEE Transactions on Image Processing, Vol. 6, No. 6, June, 1997. [6] A. Gelb, “Applied Optimal Estimation” MIT Press, 1974. [7] E. E. Kerre, M. Nachtegael, “Fuzzy Techniques in Image Processing” Verlag-Spriner Company, Germany, 2000. [8] Canny, John, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,, Vol. PAMI-8, No. 6, pp. 679698, 1986. [9] J. C. Gamio, S. J. Belongie and S. Majumdar, “Normalized Cuts in 3-D for Spinal MRI Segmentation,” IEEE Trans. Med. Imag., vol. 23,No. 1, pp. 36-44, 2004.