A wavelet based morphological mass detection and classification in mammograms

This paper presents an efficient mass detection and classification in mammogram images with the use of features extracted from the mass regions obtained by the automatic morphological based segmentation method. In this approach, the mammogram images are preprocessed to extract the breast profile and improve the contrast. The segmentation is done with combination of various morphological operations. In this approach, the wavelet features are extracted from the detected mass regions and is compared with feature extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions. Classifications of the mass regions are carried out through the Support Vector Machine (SVM) to separate the segmented regions into masses and non-masses based on the features. The methodology achieves 95% of accuracy.