Feature Extraction in Digital Mammography: An Independent Component Analysis Approach

Authors:
Athanasios Koutras;Ioanna Christoyianni;Evangelos Dermatas;George K. Kokkinakis
Affiliations:
-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

Citing 1
Cited 1

Independent component analysis: theory and applications

Independent component analysis: theory and applications

Breast Abnormality Detection Incorporating Breast Density Information Based on Independent Components Analysis

IWDM '08 Proceedings of the 9th international workshop on Digital Mammography

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Abstract

In this paper we present a new feature extraction technique for digital mammograms. Our approach uses Independent Component Analysis to find the source regions that generate the observed regions of suspicion in mammograms. The linear transformation coefficients, which result from the source regions, are used as features that describe the observed regions in an effective way. A Principal Component Analysis preprocessing step is used to reduce dramatically the features vector dimensionality and improve the classification accuracy of a Radial Basis Function neural classifier. Extensive experiments in the MIAS database using a very small dimensioned feature vector gave a recognition accuracy of 88.23% when detecting all kinds of abnormalities which outperforms significantly the accuracy of the commonly used statistical texture descriptors.