Analysis of Framelets for the Microcalcification
Mammography is used commonly to detect the cancer in breast at the early stage. The early stage of breast cancer detection helps in avoiding the removal of breast in women and even the death caused due to breast cancer. There are many computer aided softw
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Abstract Mammography is used commonly to detect the cancer in breast at the early stage. The early stage of breast cancer detection helps in avoiding the removal of breast in women and even the death caused due to breast cancer. There are many computer aided softwares that are designed to detect breast cancer but still only biopsy method is effective in predicting the exact scenario. This biopsy technique is a painful one. To avoid this, a novel classification approach for classifying microcalcification clusters based on framelet transform is proposed. The real -time mammography images were collected from Sri Ramachandra Medical Centre, Chennai, India in order to evaluate the performance of the proposed system. The GLCM features (contrast, energy and homogeneity) are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine classifier is used to classify the unknown mammograms into normal or abnormal initially and then further classifies it as benign or malignant if detected as abnormal. The result shows that framelet transform-based classification provides considerable classification accuracy.
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Keywords Framelet transform Support vector machine GLCM features (contrast, energy and homogeneity) Microcalcifications and Mammography
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1 Introduction As per the medical research reports, one of the important criterion for death in women, is due to breast cancer. Microcalcification which is a major indicator of breast malignancy is classified by many methods. A matrix consisting of wavelet K.S. Thivya (✉) Department of Electronics and Communication Engineering, Easwari Enginnering College, Chennai 600 089, India e-mail: [email protected] P. Sakthivel Department of Electronics and Communication Engineering, Anna University, Chennai, India © Springer Science+Business Media Singapore 2017 B. Raman et al. (eds.), Proceedings of International Conference on Computer Vision and Image Processing, Advances in Intelligent Systems and Computing 459, DOI 10.1007/978-981-10-2104-6_2
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K.S. Thivya and P. Sakthivel
coefficients of each image is used; then by selecting the threshold the Euclidian distance is maximized by the columns and the classification is done by using the features which are nothing but the selected columns [1]. DT CWT is used for decomposing the mammograms for different levels and SVM is used for the classification of breast cancer [2]. Two-stage wavelet transforms is applied for detecting and segmenting the granular microcalcifications [3]. Skewness and kurtosis property is used for detecting the microcalcification [4]. Texture features were extracted and the SVM is used for classification purpose [5]. Curvelet transform is employed in extracting the features of texture [6]. The Split Bregman method is used to sort out the resulting minimization problem [7]. Linearized Bregman iteration is proposed and analyzed for image deblurring in tight frame domains [8]. For image fusion, framelet transform is used [9]. Segmentation and surface reconstruction is done by usi
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