Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast
Paper in proceedings, 2009

The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).




Dynamic contrast enhanced MRI

pattern recognition



Yaniv Gal

Andrew Mehnert

Chalmers, Signals and Systems

Andrew Bradley

Dominic Kennedy

Stuart Crozier

Proc. 2009 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

132 - 139
978-1-4244-5297-2 (ISBN)

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing





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