Computerized detection of breast masses in digitized mammograms

Authors:
Celia Varela;Pablo G. Tahoces;Arturo J. Méndez;Miguel Souto;Juan J. Vidal
Affiliations:
Department of Radiology, University of Santiago de Compostela, Complejo Hospitalario de Santiago de Compostela (CHUS), Spain;Department of Electronic and Computational Science, University of Santiago de Compostela, Spain;Department of Computer Science, University of Vigo, Spain;Department of Radiology, University of Santiago de Compostela, Complejo Hospitalario de Santiago de Compostela (CHUS), Spain;Department of Radiology, University of Santiago de Compostela, Complejo Hospitalario de Santiago de Compostela (CHUS), Spain
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.