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.
authors.affiliation: DMIS, PAEC-HQ, Islamabad, Pakistan.
authors.forename: Asmatullah
authors.initials: A
authors.lastname: Chaudhry
authors.affiliation: Pattern Recognition Lab, PIEAS, P.O. Nilore, Islamabad, 45650, Pakistan. mhassan@aumc.edu.pk.|Air University, PAF Complex, Sector E-9, Islamabad, Pakistan. mhassan@aumc.edu.pk.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Hassan
authors.affiliation: Pattern Recognition Lab, PIEAS, P.O. Nilore, Islamabad, 45650, Pakistan.
authors.forename: Asifullah
authors.initials: A
authors.lastname: Khan
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D059168:Carotid Intima-Media Thickness; D002561:Cerebrovascular Disorders / Q000175:diagnosis*; D019992:Databases as Topic; D003657:Decision Making; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D012372:ROC Curve; D014465:Ultrasonics
keywords: Carotid artery image segmentation; Cerebrovascular accident; Expectation maximization; Fuzzy inference system; Intima-media thickness; Support vector machine