Intravascular Ultrasound Image Composite Segmentation using Ensemble Gabor-spatial Features

Onpans, Janya; Yookwan, Watcharaphong; Sangrueng, Jiranun; Srikamdee, Supawadee

doi:10.1109/ictc55196.2022.9952588

Cited by 1 publication

(2 citation statements)

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“…Several In Table 3, we have the average performance scores for estimating the media and lumen regions using two evaluation metrics: Hausdorff (HD) and Jaccard (JM). The comparative results show that, for the Lumen, [4] achieves the lowest HD scores, outperforming our proposed method. However, when it comes to JM, our proposed method demonstrates greater reliability compared to alternative methods, whether we're estimating the media and lumen or not.…”

Section: F Comparison With Existing Methodsmentioning

confidence: 91%

“…In another study, Lucas Lo et al [3] put forth a decoupled method for automatic IVUS blood vessel segmentation using Support Vector Machines (SVMs) and Random Forest (RF) for pixel classication and morphological feature recognition. Janya Onpans et al [4] proposed an approach to segment 1,2 The authors are with the Faculty of Informatics, Burapha University, Thailand., E-mail: benchaporn@informatics.buu.ac.th and pusit@informatics.buu.ac.th 2 Corresponding author: pusit@informatics.buu.ac.th the components of intravascular ultrasound (IVUS) images using ensemble Gabor-spatial features and a support vector machine (SVM) classification model. The proposed approach achieved promising results regarding the Jaccard Index, Hausdorff Distance, and Percentage Area Difference for segmenting the components of IVUS images.…”

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confidence: 99%

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Plaque Territory Detection in IVUS Images based on Concentration of Entropy and Gradient Magnitude via Spiral Random Walk-based Approach

Jantarakongkul,

Kulkasem

2023

ECTI-CIT Transactions

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This paper presents a simple and optimal approach for automatically identifying the location and size of plaque territories in IVUS images, thus improving plaque territory classification. Unlike existing circular-based algorithms, we leverage the anatomical structure of IVUS images to enhance accuracy. The adventitia, which constitutes the largest part of the image, serves as a landmark; however, its low contrast makes edge detection challenging. To address this issue, we enhance the brightness of the adventitia, identify and remove intima blobs, and accurately determine the media boundary. This aids in simplifying the calculation of plaque territory. To locate the plaque territory, we employ a spiral random walk-based approach that utilizes the concentration of entropy and gradient magnitude in the target area. Our approach outperforms existing methods, contributing to automated plaque analysis for cardiovascular disease diagnosis and treatment. The results show that the proposed approach achieves an accuracy of 0.89, precision of 0.81, recall of 0.77, and F1-Score of 0.83, respectively.

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Section: F Comparison With Existing Methodsmentioning

confidence: 91%