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IJSTR >> Volume 3- Issue 6, June 2014 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Segmentation And Classification Of Breast Lesions In Ultrasound Images

[Full Text]

 

AUTHOR(S)

Lokesh B, Shailaja K, Nanda S

 

KEYWORDS

Index Terms: Breast Ultrasound (BUS), Segmentation, Marker Function, Watershed, Support vector machine (SVM), Receive Operating Characteristic (ROC), Matthew's correlation coefficient (MCC).

 

ABSTRACT

Abstract: This paper proposes a new approach for computer-aided diagnosis (CAD) system with automatic contouring and texture analysis to aid in the classification of breast lesions using ultrasound. First, the goal is to remove the speckle noise while preserving important information from the lesion boundaries, anisotropic diffusion filtering is applied to the ultrasonic image. A morphological watershed transform is used for BUS image segmentation, automatically extracts the precise contour of breast lesions. 32 GLCM features are extracted from the segmented lesion. Support vector machine (SVM) classifier utilizes the selected feature vectors to identify the breast lesion as benign or malignant. Database consists of 50 images (38 Benign and 12 Malignant) and the computer-delineated margins were compared against manual outlines drawn by radiologist. The area under receive operating Characteristic (ROC) curve for proposed CAD systems using all textural features is 0.89. The classifier performance is evaluated by 4 parameters, Accuracy = 92.00, Sensitivity = 94.73, Specificity = 83.34, Matthew's correlation coefficient (MCC) = 0.78.

 

REFERENCES

[1]. Cancer facts and figures 2013. Atlanta: American cancer society; 1-8, 2013

[2]. K Drukker, M Giger, K Horsch, and M Kupinski. “Computerized lesion detection on breast ultrasound”. Medical Physics, Jan 2002.

[3]. Noble, J.A. and Boukerroui, D. “Ultrasound image segmentation: A survey”. IEEE Trans. on Medical Imaging 25, 8 , 987-1010, 2006.

[4]. Yu, Y. & Acton, S. T. “Speckle reducing anisotropic diffusion”. IEEE Transactions on Medical Imaging, 11(11), 1260-1270, 2002.

[5]. W.Gomez,L.Leija,W.C.A.Pereira and A.F.C.Infantosi. “Segmentation of Breast Nodules on Ultrasonographic images Based on Marker Controlled Watershed Transform”. ISSN 1405-5546, 2009.

[6]. Cheng, H.D., Shan, J., Ju, W., Guo, Y., and Zhang, L. “Automated breast cancer detection and classification using ultrasound images: A survey”. Pattern Recognition 43, 299-317, 2010.

[7]. Sonia H, Contreras Ortiz, Tsuicheng Chiu, Martin D.Fox. “Ultrsound image enhancement: A review”. Biomedical Signal Processing and Control 7, 419-428, 2012.

[8]. Xiaofeng Yang, Srini Tridandapani, Jonathan J. Beitler and David S. Yu, Emi J. Yoshida, Walter J. Curran and Tian Liu. “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: An in vivo study of late toxicity”, Med. Phys. 39 (9), 5732-5739, 2012.

[9]. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst.Man Cybern. Smc3, 610–621, 1973.

[10]. Bo Liu, H.D.Cheng, Jianhua Huang, Jiawei Tian, Xianglong Tang, Jiafeng Liu. “Probability density difference-based activecontour for ultrasound image segmentation”, Pattern Recognition 43, 2028–2042, 2010.

[11]. P. Perona and J. Malik, “Scale space and edge detection using anisotropic diffusion” IEEE Trans. Pattern Anal. Machine Intell., vol.12, pp. 629–639, 1990.

[12]. Madabhushi, A. and Metaxas, D.N. “Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions”. IEEE Trans. on Medical Imaging 22, 155-169, 2003.

[13]. Vincent, L. & Soille, P. “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 583-598, 1991.

[14]. Yu-Len Huang, Kao-Lun Wang, Dar-Ren Chen. “Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines”, Neural Comput & Applic 15: 164–169, 2006.

[15]. Peijun Li, Jiancong Guo, Benqin Song, and Xiaobai Xiao, “A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery,” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 4, no. 1, pp. 103 –116, march 2011.