A coronary artery segmentation method based on multiscale analysis and region growing

The choice of a segmentation method depends on several considerations, namely the nature of the image, the primitives to extract and the segmentation methods. We propose an MLP-basis neuronal approach for the choice of the segmentation method taking into account the nature of the input image. First, an evaluation of the quality of segmentation by different methods and using various criteria of evaluation was carried out. Then, a characterization of images, based on some objective parameters, was performed. The resulting descriptors will be used as input to the neuronal approach to associate each type of image with the adequate segmentation method after learning. We report the results of the intelligent segmentation method choice obtained on different databases of medical images. The discussion of these encouraging results allowed us to improve our success rate and cover all varieties of images.

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“…• 30 X-ray coronary angiography images of different patients with a resolution of 512 * 512 pixels [16].…”

Section: Resultsmentioning

confidence: 99%

MLP Neural Network Classifier for Medical Image Segmentation

Jarrar¹,

Kerkeni

Abdallah

et al. 2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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“…In addition, Yi et al limited the segmentation area to small regions and segmented each separately to address intensity variation. Recently, Kerkeni et al developed a multiscale region growing approach incorporating both a “vesselness” measure and direction information. Combined approaches have been used in which major vessels were segmented using a region‐based level‐set approach, and finer scale vessels were segmented by region growing …”

Section: Image Processingmentioning

confidence: 99%

Reconstruction of coronary circulation networks: A review of methods

et al. 2019

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Building anatomically accurate models of the coronary vascular system enables potentially deeper understandings of coronary circulation. To achieve this, (a) images at different levels of vascular network-arteries, arterioles, capillaries, venules, and veins-need to be obtained through suitable imaging modalities; and (b) from images, morphological and topological information needs to be extracted using image processing techniques. While there are several modalities that enable the imaging of large vessels, microcirculation imaging-capturing vessels having diameter lesser than 100 μm-has to date been typically confined to small regions of the heart. This spatially limited microcirculatory information has often been used within cardiac models, with the potentially erroneous assumption that it is representative of the whole organ. However, with the recent advancements in imaging and image processing, it is rapidly becoming feasible to acquire, process, and quantify microcirculation data at the scale of whole organ. In this review, we summarize the progress toward this goal followed through a presentation of the current state-of-the-art imaging and image processing techniques in the context of coronary microcirculation extraction, prominently but not exclusively, from small animals. K E Y W O R D Sconfocal imaging, coronary vascular system, deep learning, image processing, vessel extraction

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“…The filter of Frangi [17] is often considered as the current gold-standard due to its simplicity, intuitive formulation and good vascular structure enhancement of medical angiographic images [18], [19]. The thought behind this filter is that the Hessian eigenvalues are geometrically interpreted as principal vessel curvatures.…”

Section: B Multiscale Vesselness Filtermentioning

confidence: 99%

Automatic Bifurcation Detection in Coronary X-Ray Angiographies

Kerkeni¹,

Abdallah²,

Manzanera³

et al. 2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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The detection of vascular bifurcation in X-ray images is important for several medical applications. They are used as landmarks for image registration, vessel segmentation and tracking. Although many bifurcation extraction methods have been proposed in recent years, very few work deals with coronary bifurcation in X-ray images. In this paper, we present a new bifurcation detector based on the multiscale Hessian analysis. It can be seen as a scale specific Histogram of Eigenvectors weighted by the vesselness measure. Pixels with three peaks in their immediate neighbourhood are considered as bifurcation candidates. Based on this detector, a novel bifurcationness measure is proposed. The method is tested on real coronary artery angiographies and shows better results compared to other bifurcation detectors.

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A coronary artery segmentation method based on multiscale analysis and region growing

Cited by 91 publications

References 50 publications

MLP Neural Network Classifier for Medical Image Segmentation

MLP Neural Network Classifier for Medical Image Segmentation

Reconstruction of coronary circulation networks: A review of methods

Automatic Bifurcation Detection in Coronary X-Ray Angiographies

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