Adventitia segmentation in intravascular ultrasound images based on improved Snake algorithm

Wang, Yousheng; Gao, Xue; Wang, Yuting; Sun, Jinge

doi:10.1016/j.ijleo.2021.167175

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…A higher SEN value indicates better artery segmentation. The total number of pixels in the image, denoted as (n), is equal to TP + TN + FP + FN [28][29][30] (Table I).…”

Section: Resultsmentioning

confidence: 99%

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

Mardani,

Maghooli,

Farokhi

2024

Acta Phys. Pol. A

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Coronary angiography is an X-ray procedure used to examine the arteries of the heart. It provides information about the presence and severity of heart disease and helps doctors assess how well the heart is functioning. This study introduces a new technique for segmenting the coronary arteries in X-ray angiographic images using K-nearest neighbors clustering. The method involves separating thick and thin veins into distinct spaces and then merging them together. To eliminate noise and extract the vessel tree accurately, the algorithm employs morphological techniques like reconstruction, skeletonization, pruning, and dilation, as well as filters such as mean and convolution filters. The resulting segmented vessel tree contains several newly identified thin vessels that have low light intensity in the original image. The algorithm's efficacy is demonstrated by comparing the results with ground truth images. The evaluation criteria, including an accuracy of 0.9747, specificity of 0.9784, and sensitivity of 0.9049, indicate favorable performance compared to other methods. Additionally, the performance of this method is assessed using multiple lesions and instances of vessel blockages.

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“…A higher SEN value indicates better artery segmentation. The total number of pixels in the image, denoted as (n), is equal to TP + TN + FP + FN [28][29][30] (Table I).…”

Section: Resultsmentioning

confidence: 99%

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

Mardani,

Maghooli,

Farokhi

2024

Acta Phys. Pol. A

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“…Due to the popularization of imaging inspection and the ongoing progress of imaging technology, enormous image dataset has been produced [8]. In the meantime, big data is a major driver in the advancement of accuracy medicine researchers and clinicians alike have greater opportunity to engage in the evaluation and development of novel image analysis algorithm, with the final objective of generating new tools to enhance patient care [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

2023

IJIES

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Intravascular ultrasound (IVUS) is suitable for evaluating plaque and lesion morphology features and helps to make clinical decisions during the treatment of coronary artery disease (CAD). IVUS remain a gold standard in accessing atherosclerosis plaques, coronary lesions, and stenosis. Even though plaque classification by IVUS is essential for risk stratification, frame-by-frame analysis of an entire vascular segment is labor intensive. In recent times, in the field of deep learning (DL), convolutional neural network (CNN) has been intended to adaptively and automatically determine the spatial hierarchy through backpropagation. The study develops an optimal deep transfer learning based on atherosclerotic plaque and calcification on intravascular ultrasound images (ODTL-APCIUI) technique. The presented ODTL-APCIUI technique aims to classify atherosclerotic plaque and calcification. To accomplish this, the presented ODTL-APCIUI technique preprocesses the IVUS images by Gaussian filtering (GF) technique. In addition, U2Net model is applied for the segmentation process with Adam optimizer based hyperparameter tuning. Moreover, the ODTL-APCIUI technique uses DenseNet-169 model for feature extraction purposes. Furthermore, the ODTL-APCIUI technique exploits stacked autoencoder (SAE) for classification process. Finally, Harris Hawks optimization (HHO) algorithm is exploited for the hyperparameter adjustment of the SAE approach. The performance assessment of ODTL-APCIUI algorithm is tested using medical images and the results are investigated under different metrics. The experimental outcomes demonstrated that the ODTL-APCIUI technique has gained better performance with maximum accuracy of 97.19%, precision of 94.09%, sensitivity of 97.17%, specificity of 97.19%, and F-score of 92.52%.

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Research on image segmentation method based on improved Snake model

Mei

Dan

Pei

et al. 2023

Multimed Tools Appl

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Adventitia segmentation in intravascular ultrasound images based on improved Snake algorithm

Cited by 6 publications

References 9 publications

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

Research on image segmentation method based on improved Snake model

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