Paper Title
Feature Level Fusion Of Information From Mammogram And Ultrasound Images For Detection Of Micro- Calcification In Breast

Breast cancer is the most common, life-threatening cancer in women. Detection of Microcalcification plays a crucial role in diagnosis of breast cancer. Different medical modalities like mammogram, ultrasound, MRI, etc. are used in all phases of cancer detection, which provide morphological, metabolic and functional information of tissues. By integrating this extracted information from multimodalities in a meaningful way assists in clinical decision making. Proposed work helps in classification of breast microcalcification as benign or malignant for early detection of breast cancer using mammograms and Ultrasound modalities. This approach is based on the fusion of information from two modalities at feature level. Discriminative statistical, spatial and texture features of malignant microcalcifications in mammograms and ultrasound are extracted and fused. SVM classifiers are used to classify malignant microcalcifications which achieved 91.3% sensitivity. Keywords- Microcalcification, Mammogram, Ultrasound, Fusion, Dualmodality