A novel wavelet-statistics based feature detection system for detecting microcalcifications.
This paper describes a wavelet-statistics based feature detection system as applied to microcalcification detection. While a number of researches have been conducted towards microcalcification detection using wavelet analysis and auxiliary information, most of this auxiliary information was obtained from within the spatial domain. In this research, a continuous wavelet transform was used to segment features and compute energy maps of these segmented features. The kurtoses of these features were computed in the wavelet domain. This statistical information together with the energy maps forms the inputs to a rule-based classifier. Physiological information from the spatial domain was used to exclude false-positives. The system was tested using a ROI from the LLNL database. The result is one false-positive within the cluster as classified by the radiologist.
Lung Lee
Kam
K
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria 3800, Australia.
Orr
Michael
M
Lithgow
Brian
B