Fuzzy system improves the performance of wavelet-based correlation detectors

A fuzzy system is designed to classify features in the output of a wavelets-based correlation filter used for enhancing clusters of fine, granular microcalcifications-an early sign of cancer-in digitized mammograms. Each local peak in the correlation filter output is represented by a set of five fea...

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Bibliographic Details
Main Authors: Strickland, R.N., Lukins, G.J.
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrix
Detectors
Fuzzy systems
Gaussian noise
Knowledge based systems
Matched filters
Noise shaping
Object detection
Shape
System testing
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Summary:A fuzzy system is designed to classify features in the output of a wavelets-based correlation filter used for enhancing clusters of fine, granular microcalcifications-an early sign of cancer-in digitized mammograms. Each local peak in the correlation filter output is represented by a set of five features describing the shape, size and definition of the peak. These features-prominence, steepness, distinctness, compactness, and departure-are used in linguistic rules such as "IF prominence is high AND distinctness is mid-ranged AND steepness is mid-ranged THEN it might be a calcification." A fuzzy rule-based system with eight rules is trained to distinguish between microcalcifications and normal mammogram texture. Compared to wavelet processing alone, the fuzzy detection system produces an improvement of around 10% in true positive fraction when tested on a public domain mammogram database.
DOI:10.1109/ICIP.1997.632137