Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
IEEE Transactions on Medical Imaging, Volume 15, No. 4, Year 1996
Notification
URL copied to clipboard!
Description
Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. We show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data. © 1996 IEEE.
Authors & Co-Authors
Kadah, Yasser M.
United States, Minneapolis
University of Minnesota Twin Cities
Farag, Aly A.
United States, Minneapolis
University of Minnesota Twin Cities
Badawi, Ahmed M.
United States, Minneapolis
University of Minnesota Twin Cities
Youssef, Abou Bakr Mohamed
United States, Minneapolis
University of Minnesota Twin Cities
Statistics
Citations: 235
Authors: 4
Affiliations: 1
Identifiers
Doi:
10.1109/42.511750
ISSN:
02780062
Research Areas
Health System And Policy
Study Approach
Quantitative