Analysis and Classification of Breast density using Mammographic Images

Abstract

The present research work has been carried out with an aim to enhance the diagnostic potential of mammography imaging modality for classification of breast density. To achieve this objective, the design and implementation of an interactive framework for classification of breast density using digitized screen film mammograms are proposed in the present study. The research objectives for the present work were formulated keeping in view the needs of the radiologists, based on the practical difficulties faced by them in routine clinical practice.The fact has been also observed that the breast density classification systems can be designed using either segmented breast tissue or a predefined ROI on benchmark dataset or dataset collected by an individual research group. Accordingly, the present study performed for developing an efficient 4 class and 2 class breast density classification systems using ROI based approach on a benchmark dataset. newlineThe study was conducted on a comprehensive image dataset of 480 MLO view digitized screen film mammograms. The same set of mammographic images have been used for 2 class breast density classification by considering cases belonging to BIRADS I and BIRADS II classes in fatty image class and cases belonging to BIRADS III and BIRADS IV classes in dense image class.A classification accuracy of 84.1 percent has been achieved by using hybrid hierarchical classification framework which is consisting of less number of classifier with respect to another module. newlineThus it has been concluded that the principal component analysis and multiresolution texture descriptors based computerized framework should be used in the clinical practice for the discrimination between fatty and dense mammograms using digitized screen film mammograms newline newline