Classification of benign and malignant masses in breast mammograms

An accurate and efficient computer-aided mammography diagnosis system plays an important role as a second opinion to assist radiologists. Finding an accurate and robust computer-aided diagnosis system for classification of the abnormalities in the mammograms as malignant or benign still remains a ch...

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Bibliographic Details
Published in2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) pp. 228 - 233
Main Authors Šerifović-Trbalić, A., Trbalić, A., Demirović, D., Prljača, N., Cattin, P. C.
Format Conference Proceeding
LanguageEnglish
Published MIPRO 01.05.2014
Subjects
Breast cancer
computer-aided diagnosis
Feature extraction
Image segmentation
mammography
neural network
Neural networks
segmentation
Shape
Zernike moments
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Summary:An accurate and efficient computer-aided mammography diagnosis system plays an important role as a second opinion to assist radiologists. Finding an accurate and robust computer-aided diagnosis system for classification of the abnormalities in the mammograms as malignant or benign still remains a challenge in the digital mammography. In this paper, a fully autonomous classification system is presented and it consists of the three stages. The input Regions of Interest (ROIs) are obtained using an efficient Otsu's N thresholding and further subjected to a number of preprocessing stages. After preprocessing stage, from the ROIs, a group of 32 Zernike moments with different orders and iterations have been extracted. These moments have been applied to the neural network classifier. The experimental results show that the proposed algorithm is efficient comparing to the ground truth table given in the Mammography Image Analysis Society (MIAS) database.
DOI:10.1109/MIPRO.2014.6859566