Cyst Segmentation in Texture Feature Space in Ultrasound Breast Images

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1 Cyst Segmentation in Texture Feature Space in Ultrasound Breast Images Maitri R Bhat, Nanda S Dept of Instrumentation Technology SJCE, Mysore rbmaitri123@gmail.com, nanda_prabhu@yahoo.co.in Abstract Object segmentation is a crucial step in medical image analysis. Active contour method of segmentation gives more accurate results but depends largely on initial contour selection. In this paper we are concerned with breast cyst segmentation based on texture features and also using Chan-vese method. Image features are obtained by convolving first order feature kernels of mean, standard deviation and entropy with image. Performance evaluation of segmentation is done using measures like Area Error Rate, DICE coefficient, sensitivity and Hausdroff distance. Index Terms Chan-vese level set; image features; ultrasound images; area error rate 1. INTRODUCTION Cysts are fluid-filled sacs that grow inside the breasts. These sacs form when normal milk glands in the breast get bigger. They're often described as round or oval lumps with distinct edges. Breast cysts can be painful and may be worrisome but are generally benign[guyer,(1992)]. They are most common in premenopausal women in their 30s or 40s.Cystic breast disease has been recognised as most frequent female lesion and is generally benign [Huff,(2009)] Many works have been carried out and still continued to segment breast mass and to distinguish malignant mass from benign. Segmentation of benign breast cyst is first step in this direction. Active contour models are considered useful in segmentation and applied on ultrasound images to segment cyst part. In this paper, level set method of active contour method (or snakes) is applied to segment breast cyst in ultrasound images. Also, developing the idea suggested by Moraru et.al [Moraru, 2013] the active contour model is applied over texture features obtained by convolving first order features with the image. The results were analysed by using Area Error Rate (AER), DICE coefficient, sensitivity and Hausdroff distance. 2. MATERIAL AND METHODOLOGY A set of 15 cystic Breast Ultrasound (BUS) images were obtained from hospital. They are acquired by Philips HD 15 using 7.5MHz transducer. The implementation method is summarized in block diagram shown in Fig 1. The BUS image is passed through wiener filter to remove noise added mainly due to respiration of patient. Then, filtered image is contrast enhanced using adaptive histogram equalization. Then those images are converted to texture features as described in following section. Images are convolved using four kernels of size 3 x 3, 5 x 5, 7 x 7, and 9x 9 to get texture features. Three texture features Mean, standard deviation, entropy are used. Another mask used is Qui s mask [Qui,(2005)] which is atypical mask and is given by, M = 1 Eq.(1) where should satisfy the condition 4 ( + (/2 + / 2 + /2 2)) + 8 (/ 5) = 1 and its value is

2 Fig1. Block diagram of segmentation and evaluation scheme Active contour method is applied on each Image feature obtained for fixed number of iterations [Whitakar,(1998)]. Chan-vese level set method is used for active contour. Chan- vese level set method performs better than gradient method [Wang (2010)] because it utilizes global image statistics inside and outside the curve and works well even with weak boundaries and in the presence of noise. Region based measures used for validating segmentation are Area Error Rate (AER), dice coefficient and Sensitivity. Boundary based measure of Hausdorff distance is also calculated. Area Error Rate estimates difference between occupied areas and is used to quantitatively assess the segmentation accuracy. Area Error Rate is given by = 100 % Eq(2) Where represents the number of pixels within the area URand is the amount of pixels within the area IR, while isthe number of pixels in the manually extracted area MSR. LVsnakedenotes the automatically segmented area and LVmanual is the fragment extracted manually. UR = LVsnake Lvmanualand IR = Lvsnake Lvmanual. Dice coefficient is used to compare similarity of two samples and is calculated as = Eq(3) Sensitivity is calculated by using following formula = Eq(4) Hausdroff distance is a measure for the dissimilarity of two shapes and is used here for comparing the boundary detected. It is given by, =, Eq(5) Where ASR is Automatically Segmented Area and MSR is Manually Segmented Area and is boundary of ASR and is boundary of MSR. 3. RESULTS The breast images contain breast muscle tissues (as bright areas) and suspicious areas (as dark regions). The image is filtered and contrast enhanced as shown in Fig 2.Fig 2(c) shows initial contour selected by the user. Level set segmentation is applied on preprocessed image and features mean, standard deviation and entropy are obtained using kernels of sizes 3 X 3,5 X 5,7 X 7 and 9 X 9 shown respectively in Fig 3,Fig4,Fig 5 and Fig

3 (a) (b) (c) Fig 2. Original Image (a), filtered and contrast enhanced image (b),selection of initial contour(c). (a1) (a2) (a3) (a4) Fig 3. Segmentation using Image features, Original Image (a1), Image features using kernel size 3 X 3- mean (a2), standard deviation (a3), entropy (a4). (b1) (b2) (b3) (b4) Fig 4.Segmentation using Image features, Original Image (b1), Image features using kernel size 5 X 5- mean (b2), standard deviation (b3), entropy (b4). Applying segmentation on image processed with Que s mask gives result as in Fig 7. The red contours represent automatically detected boundaries 4. DISCUSSIONS Evaluation measures Area Error Rate (AER), DICE coefficient, sensitivity and Hausdroff distance (HD) are tabulated in table 1 for segmented original image and for image filtered using Qui s mask. Also, these are calculated for texture features mean, standard deviation and entropy using four kernels 202

4 (3X3, 5X5, 7X7, and 9X9) in table 2 and table 3. According to Moraru et al. [Moraru(2013)] using entropy filter gives reduced AER. In our experiments AER is prominently reduced using Mean kernel and Qui s mask compared to Entropy and Standard deviation. DICE coefficient gives measure of similarity between manually segmented region and region segmented using texture features. DICE and sensitivity values are in [0,1] range. Segmentation performed on pre-processed BUS image, image filtered using Qui s mask and using mean texture feature have comparatively higher DICE and sensitivity values than segmentation results obtained using texture features standard deviation and entropy. Hausdroff distance (HD) is used as evaluation of boundary obtained automated method. Manual segmentation is done by radiologist. HD is prominently reduced using Mean kernel and Qui s mask compared to Entropy and Standard deviation. (c1) (c2) (c3) (c4) Fig 5. Segmentation using Image features, Original Image (c1), Image features using kernel size 7 X 7- mean (c2), standard deviation (c3), entropy (c4) (d1) (d2) (d3) (d4) Fig 6. Segmentation using Image features, Original Image (d1), Image features using kernel size 9 X 9- mean (d2), standard deviation (d3), entropy (d4) (e1) (e2) Fig 7. Segmentation using Image filtered with Que s mask, Image filtered with Que s mask (e1), boundary detected (e2). 203

5 Table 1. Segmentation validation for Qui s kernel. AER DICE Sensitivity Hausdroff Distance Original Image Qui's kernel Table 2. Features AER and DICE derived with different kernels AER DICE 3X3 5X5 7X7 9X9 3X3 5X5 7X7 9X9 Imean IstdDev Ientropy Table 3.Features sensitivity and HD derived with different kernels Sensitvity HD 3X3 5X5 7X7 9X9 3X3 5X5 7X7 9X9 Imean IstdDev Ientropy CONCLUSION This paper presents an approach to quantitatively assess the performance of segmentation of cyst in breast ultrasound images using texture features and Chan-vese level set method. The experimental results show that error rates are higher in texture features standard deviation and entropy. The mean texture feature and Qui s mask gives less error rate and hence more accurate results. ACKNOWLEDGMENTS We would like to express our gratitude to Dr Rajesh (JSS Hospital, Mysore), for his support in data collection and valuable suggestions during the development of this work. His willingness to give his time so generously has been very much appreciated. REFERENCES [2] Xiao-Feng Wanga, De-Shuang Huanga,Huan Xua (2010). An efficient local Chan Vese model for image segmentation, ScienceDirect [3] Qiu PH.(2005) Image processing and jump regression analysis. Hoboken, NJ, USA:Published by John Wiley & Sons, Inc.;. p [4] Ross. T Whitakar (1998). A Level-Set Approach to 3D Reconstruction from Range Data, International Journal of Computer Vision 29(3), (1998) [5] John G. Huff,(2009)The Sonographic Findings and Differing Clinical Implications of Simple, Complicated, and Complex Breast Cysts. Medscape, [6] Peter B Guyer, Keih C Dewbury and David O Cosgrove(1992), Abdominal and General Ultrasound Volume 2 : The Breast, [1] Luminita Morarua, Simona Moldovanua, Anjan Biswas (2013). Optimization of breast lesion segmentation in texture feature space approach, Med Eng Phys. 204