Paper Info
Title
Robust segmentation and intelligent decision system for cerebrovascular disease.
Abstract
Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation.
Year
DOI
Venue
2016
10.1007/s11517-016-1481-1
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Carotid artery image segmentation,Cerebrovascular accident,Expectation maximization,Fuzzy inference system,Intima-media thickness,Support vector machine
Histogram,Computer vision,Scale-space segmentation,Pattern recognition,Expectation–maximization algorithm,Segmentation,Support vector machine,Artificial intelligence,Classifier (linguistics),Discriminative model,Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
54
12
1741-0444
Citations 
PageRank 
References 
0
0.34
13
Authors
3
Name
Order
Citations
PageRank
Asmatullah Chaudhry1978.80
Mehdi Hassan2576.11
khan362344.09