Tissue density classification in mammographic images using local features - Kutluk, Sezer; Günsel, Bilge (IEEE, 2013)
In breast cancer cases, it is known that the ratio of correct diagnosis is affected by the breast tissue density. For this reason, automatic tissue density classification is an important process in diagnosis. In this work a method for classification of breast tissue density from mammographic images is proposed. The objective of the method is to determine which class, namely fatty, fatty-glandular and dense-glandular, the breast tissue belongs to. For this purpose, SIFT algorithm is used as the local feature extraction method, and LVQ algorithm is used for supervised classification. Test results on the MIAS dataset demonstrate that the code vectors corresponding to bag of SIFT features of each class can successfully model the breast tissue and the classification accuracy over 90% is achieved by LVQ.