Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification.

Full Abstract

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.

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Author information

Author/s: Tsui, Patrick P C (PP); Basir, Otman A (OA);

Affiliation: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. pcptsui(-atsign-)engmail.uwaterloo.ca <pcptsui(-atsign-)engmail.uwaterloo.ca>

Journal and publication information

Publication Type: Journal Article

Journal: Ultrasonics (Ultrasonics), published in Netherlands. (Language: eng)

Reference: 2006-Dec; vol 45 (issue 1-4) : pp 1-14

Dates: Created 2006/11/24; Completed 2007/08/21;

PMID: 16844168, status: MEDLINE (last retrieval date: 12/26/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Food Analysis - methods
Food Contamination - analysis
Foreign Bodies - ultrasonography
Humans

Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Materials Testing - methods
Ultrasonography - methods

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