Title :
Fuzzy system improves the performance of wavelet-based correlation detectors
Author :
Strickland, Robin N. ; Lukins, Gregory J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA
Abstract :
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
Keywords :
correlation methods; diagnostic radiography; fuzzy systems; image enhancement; knowledge based systems; wavelet transforms; cancer; compactness; correlation filter output; departure; digitized mammograms; distinctness; features classification; fuzzy detection system; fuzzy rule-based system; fuzzy system; granular microcalcifications; linguistic rules; normal mammogram texture; performance; prominence; public domain mammogram database; steepness; wavelet processing; wavelet-based correlation detectors; wavelets-based correlation filter; Covariance matrix; Detectors; Fuzzy systems; Gaussian noise; Knowledge based systems; Matched filters; Noise shaping; Object detection; Shape; System testing;
Conference_Titel :
Image Processing, 1997. Proceedings., International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8183-7
DOI :
10.1109/ICIP.1997.632137
