Detection of Masses in Mammograms using Enhanced Multilevel-Thresholding Segmentation and Region Selection based on Rank

A. Rojas Domínguez and A.K. Nandi (UK)


Medical Image Processing, Breast Cancer, Breast Masses, Mammography, Tumor Detection.


A method for detection of masses in mammograms is pre sented. This method follows the general scheme of: (1) preprocessing of the image to increase the signal-to-noise ratio of the lesions being detected, (2) segmentation of all potential lesions, and (3) elimination of false-positive find ings. An algorithm for enhancement of mammograms is proposed; this algorithm has the objective of improving the segmentation of distinct structures in mammograms, using wavelet decomposition and reconstruction, morpho logical operations, and local scaling. After preprocessing, the segmentation of regions is performed via conversion to binary images at multiple threshold levels (multilevel thresholding segmentation), and a set of features is com puted from each of the segmented regions. Finally, a rank ing system based on the features computed is employed to select the regions representing abnormalities. The method was tested on 57 mammographic images of masses from the mini-MIAS database, including circumscribed, spicu lated, and ill-defined masses. In this test, the proposed method achieved a sensitivity of 80% at 2.3 false-positives (FPs) per image.

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