Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification.
This paper proposes a novel technique for automatic ultrasound non-destructive foreign body (FB) detection and classification. A signal registration process is introduced to eliminate shift variations commonly encountered in ultrasound signals. Information theory based methods are then developed for wavelet basis selection and feature extraction to facilitate robust FB classification. Probabilistic neural networks are used for FB classification. Experimental results confirm that the wavelet basis selected by the proposed method improves the FB classification accuracy. It is concluded that low order wavelet bases have better ability to distinguish classes with great similarities than their higher order counterparts, while the reverse is true for more divergent classes.
Tsui
Patrick P C
PP
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. pcptsui@engmail.uwaterloo.ca <pcptsui@engmail.uwaterloo.ca>
Basir
Otman A
OA
D000465:Algorithms; D005504:Food Analysis / Q000379:methods*; D005506:Food Contamination / Q000032:analysis*; D005547:Foreign Bodies / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008422:Materials Testing / Q000379:methods*; D014463:Ultrasonography / Q000379:methods*