An automatic segmentation of calcified tissue in forward-looking intravascular ultrasound images

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-10-28 DOI:10.1016/j.bspc.2024.107095
Ziyu Cui, Zhaoju Zhu, Peiwen Huang, Chuhang Gao, Bingwei He
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Abstract

The assessment of images of the coronary artery system plays a crucial part in the diagnosis and treatment of cardiovascular diseases (CVD). Forward-looking intravascular ultrasound (FL-IVUS) has a distinct advantage in assessing CVD due to its superior resolution and imaging capability, especially in severe calcification scenarios. The demarcation of the lumen and media-adventitia, as well as the identification of calcified tissue information, constitute the initial steps in assessing of CVD such as atherosclerosis using FL-IVUS images. In this research, we introduced a novel approach for automated lumen segmentation and identification of calcified tissue in FL-IVUS images. The proposed method utilizes superpixel segmentation and fuzzy C-means clustering (FCM) to identify regions that potentially correspond to lumina. Furthermore, connected component labeling and active contour methods are employed to refine the contours of lumina. To handle the distinctive depth information found in FL-IVUS images, ellipse fitting and region detectors are applied to identify areas with calcified tissue. In our dataset consisting of 43 FL-IVUS images, this method achieved mean values for Jaccard measure, Dice coefficient, Hausdorff distance, and percentage area difference at 0.952 ± 0.016, 0.975 ± 0.008, 0.296 ± 0.186, and 0.019 ± 0.010, respectively. Furthermore, when compared with traditional segmentation approaches, the proposed approach yields higher images quality. The test results demonstrate the effectiveness of this innovative automated segmentation technique for detecting the lumina and calcified tissue in FL-IVUS images.
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自动分割前视血管内超声图像中的钙化组织
冠状动脉系统图像的评估在心血管疾病(CVD)的诊断和治疗中起着至关重要的作用。前瞻性血管内超声(FL-IVUS)因其卓越的分辨率和成像能力,在评估心血管疾病方面具有明显优势,尤其是在严重钙化的情况下。使用前视血管内超声图像评估动脉粥样硬化等心血管疾病的最初步骤是划分管腔和介质-内膜,以及识别钙化组织信息。在这项研究中,我们引入了一种新方法,用于在 FL-IVUS 图像中自动分割管腔和识别钙化组织。该方法利用超像素分割和模糊 C-means 聚类(FCM)来识别可能与管腔相对应的区域。此外,还采用了连接分量标记和主动轮廓方法来细化腔隙的轮廓。为了处理 FL-IVUS 图像中的独特深度信息,我们采用了椭圆拟合和区域检测器来识别钙化组织区域。在由 43 张 FL-IVUS 图像组成的数据集中,该方法的 Jaccard 测量、Dice 系数、Hausdorff 距离和面积差异百分比的平均值分别为 0.952 ± 0.016、0.975 ± 0.008、0.296 ± 0.186 和 0.019 ± 0.010。此外,与传统的分割方法相比,所提出的方法能获得更高的图像质量。测试结果证明了这种创新的自动分割技术在检测 FL-IVUS 图像中的管腔和钙化组织方面的有效性。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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