Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique

• Published in: Biomedical signal processing and control Vol. 40; pp. 462 - 472
• Main Authors: Nagaraj, Y., Madipalli, Pardhu, Rajan, Jeny, Kumar, P. Krishna, Narasimhadhan, A.V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2018
• Subjects: Intima media thickness; Optimized Bayesian non local mean filter; Total variation L1 norm; Wind driven optimization technique; Wind driven optimization technique; Optimized Bayesian non local mean filter; Total variation L1 norm; Intima media thickness
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2017.08.009

•The key contribution of the paper is to propose a novel approach for fully automatic ROI extraction in carotid ultrasound images.•This paper presents a novel approach for threshold based segmentation of intima media complex using wind driven optimization algorithm.•Proposed method estimates intima media thickness (IMT) for carotid ultrasound images with better accuracy as compared with state-of-the-art algorithms.
Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images.