An efficient digital mammogram image classification using DTCWT and SVM

Authors:
S. Deepa;V. Subbiah Bharathi
Affiliations:
Sathyabama University, Chennai, India;DMI College of Engineering, Chennai, India
Venue:
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Year:
2012

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Abstract

Mammography is the most widely used and the most efficient method for detection of breast cancer. Computer Aided Diagnostic (CADx) systems are used to aid the radiologists in interpreting the mammograms. In this paper we propose an efficient CADx system for classifying the digital mammograms as benign (Non Cancerous) or Malignant (Cancerous). Dual Tree Complex Wavelet Transform (DT-CWT) has shown a good performance in applications that involve medical image processing due to more data phase information, shift invariance and directionality than other wavelet transforms. The ROI (Region Of Interest) image is decomposed using DTCWT and statistical features are extracted and the images are classified using nonlinear Support Vector Machines (SVM). Experiments are carried out on digital mammogram images derived from MIAS (Mammographic Image Analysis Society) mini mammographic database. Classification accuracy of 93.34% is achieved using the proposed method and the results prove that the proposed method can be used as an efficient tool to assist the radiologist in classifying the large number of mammograms generated during widespread screening.