Document Detail


Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment.
MedLine Citation:
PMID:  23292291     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In the case of carotid atherosclerosis, to avoid unnecessary surgeries in asymptomatic patients, it is necessary to develop a technique to effectively differentiate symptomatic and asymptomatic plaques. In this paper, we have presented a data mining framework that characterizes the textural differences in these two classes using several grayscale features based on a novel combination of trace transform and fuzzy texture. The features extracted from the delineated plaque regions in B-mode ultrasound images were used to train several classifiers in order to prepare them for classification of new test plaques. Our CAD system was evaluated using two different databases consisting of 146 (44 symptomatic to 102 asymptomatic) and 346 (196 symptomatic and 150 asymptomatic) images. Both these databases differ in the way the ground truth was determined. We obtained classification accuracies of 93.1 and 85.3 %, respectively. The techniques are low cost, easily implementable, objective, and non-invasive. For more objective analysis, we have also developed novel integrated indices using a combination of significant features.
Authors:
U Rajendra Acharya; Muthu Rama Krishnan Mookiah; S Vinitha Sree; David Afonso; Joao Sanches; Shoaib Shafique; Andrew Nicolaides; L M Pedro; J Fernandes E Fernandes; Jasjit S Suri
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-6
Journal Detail:
Title:  Medical & biological engineering & computing     Volume:  -     ISSN:  1741-0444     ISO Abbreviation:  Med Biol Eng Comput     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-7     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7704869     Medline TA:  Med Biol Eng Comput     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore, aru@np.edu.sg.
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