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Updated: May 10, 2026
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Detection of masses based on asymmetric regions of digital bilateral mammograms using spatial description with
Daniel Rodrigues Ericeira1, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva
1Federal University of Maranhão-UFMA, Applied Computing Group-NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. daniel.ericeira@gmail.com
Abstract:
A mammogram is an examination of the breast intended to prevent and diagnose breast cancer. In this work we propose a methodology for detecting masses by determining certain asymmetric regions between pairs of mammograms of the left and the right breast. The asymmetric regions are detected by means of structural variations between corresponding regions, defined by a spatial descriptor called cross-variogram function. After determining the asymmetric regions of a pair of images, the variogram function is applied to each asymmetric region separately, for classification as either mass or non-mass. The first stage of the methodology consists in preprocessing the images to make them adequate for registration. The following step performs the bilateral registration of pairs of left and right breasts. Pairs of corresponding regions are listed and their variations are measured by means of the cross-variogram spatial descriptor. Next, a model is created to train a Support Vector Machine (SVM) using the values of the cross-variogram function of each pair of windows as features. The pairs of breasts containing lesions are classified as asymmetric regions; the remaining ones are classified as symmetric regions. From the asymmetric regions, features are extracted from the variogram function to be used as tissue texture descriptors. The regions containing masses are classified as mass regions, and the other ones as non-mass regions. Stepwise linear discriminant analysis is used to select the most statistically significant features. Tests are performed with new cases for the final classification as either mass or non-mass by the trained SVM. The best results presented in the final classification were 96.38% of accuracy, 100% of sensitivity and 95.34% of specificity. The worst case presented 70.21% of accuracy, 100% of sensitivity and 67.56% of specificity. The average values for all tests were 90.26% of accuracy, 100% of sensitivity and 85.37% of specificity.
