Automatic Segmentation of Intima Media Complex in Common Carotid Artery using Adaptive Wind Driven Optimization

Pardhu Madipalli, S. Kotta, Harish Dadi, N. Y., A. C S, A. V. Narasimhadhan
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引用次数: 5

Abstract

Cardiovascular diseases have been one of the leading causes of death and have been increasing in much of the developing world. Atherosclerosis, the accumulation of plaque on artery walls is the major for cardiovascular diseases. This is diagnosed by measuring the thickness of IMC of common carotid artery (CCA) in ultrasound images. In this paper, we present a completely automatic technique for segmentation of IMC in ultrasound images of CCA. The image is segmented using adaptive wind driven optimization (AWDO) technique. The denoising filter based on Bayesian least square approach and a robust enhancement technique is used in the pre-processing stage. The proposed method is evaluated on 60 ultrasound images and is compared with the state-of-the-art methods. The experimental results show that the proposed method yields better results as compared to other methods.
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应用自适应风驱动优化自动分割颈总动脉内膜中膜复合体
心血管疾病一直是导致死亡的主要原因之一,并且在许多发展中国家一直在增加。动脉粥样硬化,即斑块在动脉壁上的积聚,是心血管疾病的主要诱因。这是通过测量颈总动脉(CCA)超声图像的IMC厚度来诊断的。在本文中,我们提出了一种完全自动分割CCA超声图像中IMC的技术。采用自适应风驱动优化(AWDO)技术对图像进行分割。预处理阶段采用了基于贝叶斯最小二乘法的去噪滤波器和鲁棒增强技术。所提出的方法是评估60超声图像,并与最先进的方法进行比较。实验结果表明,与其他方法相比,该方法取得了更好的效果。
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