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Proceedings Paper

Automatic tissue characterization from ultrasound imagery
Author(s): Yasser M. Kadah; Aly A. Farag; Abou-Bakr M. Youssef; Ahmed M. Badawi
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Paper Abstract

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

Paper Details

Date Published: 20 August 1993
PDF: 12 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150145
Show Author Affiliations
Yasser M. Kadah, Univ. of Louisville (United States)
Aly A. Farag, Univ. of Louisville (United States)
Abou-Bakr M. Youssef, Cairo Univ. (Egypt)
Ahmed M. Badawi, Cairo Univ. (Egypt)


Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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