Texture Based Classification of Mass Abnormalities in Mammograms

Authors:
S. Baeg;N. Kehtarnavaz
Affiliations:
-;-
Venue:
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Year:
2000

Citing 3
Cited 2

Machine vision

Machine vision
Neural network design

Neural network design
Development of New Schemes for Detection and Analysis of Mammographic Masses

IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)

Approaches for automated detection and classification of masses in mammograms

Pattern Recognition
A new preprocessing filter for digital mammograms

ICISP'10 Proceedings of the 4th international conference on Image and signal processing

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Abstract

This paper presents a scheme for the classification of mass abnormalities in digitized or digital mammograms based on two novel images texture features. The first texture feature provides a measure of smoothness/denseness and is obtained by applying a morphological operator to maxima/minima image points. The second texture feature reflects a measure of architectural distortion and is derived from image gradients. A three-layer back propagation neural network is used as the classifier. The performance of the classification scheme is evaluated by carrying out a receiver operating characteristic (ROC) analysis. Classification of 150 biopsy proven masses into benign and malignant classes resulted in a ROC area of 0.91. The results obtained demonstrate the potential of using this scheme as an electronic second opinion to lower the number of unnecessary biopsies.