Journal
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Volume 51, Issue 5, Pages 513-523Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-012-1019-0
Keywords
Atherosclerosis; Carotid ultrasound; Classification; Fuzzy texture; Index; Plaque characterization; Trace transform
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available