20 August 1993 Automatic tissue characterization from ultrasound imagery
Author Affiliations +
Proceedings Volume 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques; (1993) https://doi.org/10.1117/12.150145
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yasser M. Kadah, Aly A. Farag, Abou-Bakr M. Youssef, Ahmed M. Badawi, "Automatic tissue characterization from ultrasound imagery", Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150145; https://doi.org/10.1117/12.150145

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