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Issue title: Special section: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic
Article type: Research Article
Authors: Shiji, T. P.a; * | Remya, S.a | Lakshmanan, Rekhab | Pratab, Tharac | Thomas, Vinua
Affiliations: [a] Department of Electronics Engineering, Model Engineering College, Kochi, India | [b] Department of Computer Engineering, KMEA College of Engineering, Kerala, India | [c] Lakeshore Hospital, Kochi, Kerala, India
Correspondence: [*] Corresponding author. T. P. Shiji, Department of Electronics Engineering, Model Engineering College, Kochi, India. [email protected]
Abstract: Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising.
Keywords: Breast ultrasound, Shearlet transform, tumour detection, wavelet modulus maxima, SVM classifier
DOI: 10.3233/JIFS-179709
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6279-6290, 2020
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