Assessment of image features for vessel wall segmentation in intravascular ultrasound images

Vercio, Lucas Lo; Orlando, José Ignacio; Fresno, Mirta Mariana del; Larrabide, Ignacio

doi:10.1007/s11548-015-1345-4

Cited by 30 publications

(19 citation statements)

References 27 publications

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“…Impediments in IVUS image segmentation are utilized to provide topological constraints on the boundary. Compared to the time-consuming extraction of features and training of classifiers [3][4][5][6][7][8], unsupervised clustering with post-assignment is fast and requires no labeled data. The iterative update of indicators D g , D a , and D s can quickly mine valuable information in the image.…”

Section: Discussionmentioning

confidence: 99%

“…Step (7) ROIs outside MAB and the ROI containing R c are deleted in B 2o , the result is denoted by B 2 (Figure 5h).…”

Section: Roi Assignment Algorithmmentioning

confidence: 99%

“…SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset. sparse representation framework [4], support vector machines [5][6][7], or artificial neural networks [8]. An assessment of the capability of different features for segmenting IVUS images was given by Lo Vercio et al [7].…”

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confidence: 99%

“…sparse representation framework [4], support vector machines [5][6][7], or artificial neural networks [8]. An assessment of the capability of different features for segmenting IVUS images was given by Lo Vercio et al [7]. In other deformable model-based methods, a global intensity term and an oriented smooth gradient were developed for the initial border extraction [9].…”

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confidence: 99%

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IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

et al. 2019

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Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset.

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Section: Discussionmentioning

confidence: 99%

“…Step (7) ROIs outside MAB and the ROI containing R c are deleted in B 2o , the result is denoted by B 2 (Figure 5h).…”

Section: Roi Assignment Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

et al. 2019

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“…Most of the researchers utilized JI and PAD as performance evaluation in evaluating the dissimilarity. The outcome demonstrates that segmentation by means of the formulated scheme is extremely superior than the Danilo Samuel Jodas et al , equivalent to Mehdi Faraji et al On the other hand, lower than Lo Vercio et al Hannah Sofian et al, Destrempes et al and Shanhui Sun et al in[20][21][22][23][24][25].…”

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confidence: 87%

Performance Assessment of K-Means and KNN Algorithm for the Detection of Borders and Extraction of Plaque Area from IVUS Images

Mahadevi*,

Sivakumar

2020

IJRTE

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The coronary artery vascular disease of atherosclerosis, which made the blood vessel artery wall harden and narrow. The vascular wall disease quantitatively analyzed and diagnosed by an intravascular ultrasound (IVUS) image. The quantitative investigations of coronary atherosclerosis by means of IVUS and manual recognition of wall and plaque borders are restricted by the need for observers with considerable understanding and the tedious environment of manual border detection. To improve and provide more detailed vessel and plaque information for better diagnosis and assessment go for an automated segmentation. An automated construction for the purpose of perceiving lumen and media-adventitia borders in IVUS images was effectively formulated. An effectual unsupervised K-Means clustering scheme refine the borders with morphological operations, later an IVUS data samples area classified using supervised KNN(K-Nearest Neighbor) classifier to extract the plaque feature. The performance of contour metric measurements in terms of Jaccard Index (JI), percentage area difference (PAD), area error (AE), dice index (DI), false positive ratio (RFP), in addition to false negative ratio (RFN) are computed for evaluation and variation.

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Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset

et al. 2017

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Purpose: Diabetic retinopathy (DR) is one of the most widespread causes of preventable blindness in the world. The most dangerous stage of this condition is proliferative DR (PDR), in which the risk of vision loss is high and treatments are less effective. Fractal features of the retinal vasculature have been previously explored as potential biomarkers of DR, yet the current literature is inconclusive with respect to their correlation with PDR. In this study we experimentally assess their discrimination ability to recognize PDR cases. Methods: A statistical analysis of the viability of using three reference fractal characterization schemes-namely box, information and correlation dimensions-to identify patients with PDR is presented. These descriptors are also evaluated as input features for training 1 and 2 regularized logistic regression classifiers, to estimate their performance. Results: Our results on MESSIDOR, a public data set of 1200 fundus photographs, indicate that patients with PDR are more likely to exhibit a higher fractal dimension than healthy subjects or patients with mild levels of DR (p ≤ 1.3 × 10 −2 ). Moreover, a supervised classifier trained with both fractal measurements and red lesion based features reports an area under the ROC curve of 0.93 for PDR screening and 0.96 for detecting patients with optic disc neovascularizations. Conclusions: The fractal dimension of the vasculature increases with the level of DR. Furthermore, PDR screening using multiscale fractal measurements is more feasible than using their derived fractal dimensions. Code and further resources are provided at https: //github.com/ignaciorlando/fundus-fractal-analysis.

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Assessment of image features for vessel wall segmentation in intravascular ultrasound images

Abstract: Noise-reduction filters and Haralick's textural features denoted their relevance to identify lumen and background. Laws' textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process.

Cited by 30 publications

References 27 publications

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

Performance Assessment of K-Means and KNN Algorithm for the Detection of Borders and Extraction of Plaque Area from IVUS Images

Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset

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