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Authors
Affiliations
1 Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
2 Dean of Computing, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
3 Dean & Professor, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
Source
Digital Image Processing, Vol 4, No 12 (2012), Pagination: 673-677
Abstract
Breast cancer is one of the most dangerous diseases that cause innumerable fatal in the female society. Early detection is the only way to reduce the mortality. Due to variety of factors sometimes manual reading of mammogram results in misdiagnosis. So that the diagnosis rate varies from 65% to 85%. Various Computer Aided Detection techniques have been proposed for the past 20 years. Even then the detection rate is still not high. The proposed method consists of the following steps Preprocessing, Segmentation, Feature extraction and Classification. Noise, Artifact and Pectoral region are removed in preprocessing step. Contrast enhancement, alarm region generation and Region growing method is used to segment the mass region. Segmented features are extracted using Gray Level Co-occurrence Matrix. Extracted features are classified using Support Vector Machine. Performance of the proposed system is evaluated using partest method. Proposed algorithm shows 95.2% sensitivity and 94.4% Specificity. The proposed algorithm is fully automatic and will be helpful in assisting the radiologists to detect the malignancy efficiently.
Keywords
Mammogram, Computer Aided Detection, Adaptive Histogram, Segmentation, Feature Extraction, Support Vector Machine.
