Robust Centerline Extraction Framework Using Level Sets

Hassouna, M. Sabry; Farag, Aly A.

doi:10.1109/cvpr.2005.306

Cited by 93 publications

(52 citation statements)

References 16 publications

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“…Gradient flux-based algorithms are quite popular (e.g. [175], [176], [177], [178], [179], [180]) since gradient flux allows overcoming intensity inhomogeneities, resulting in the segmentation of the entire vascular tree without contour leakages.…”

Section: ) Parametricmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

454

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: ) Parametricmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

454

263

View full text Add to dashboard Cite

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“…(c) For every point representing the reference shape A, generate an orthogonal wave by solving the Eikonal equation using the fast marching level sets at the speed function F (x, y) = exp(D(x, y)). For more detail see [26]. (d) Track the point with the maximum curvature for each propagating wave front (see Figs.…”

Section: Shape Priormentioning

confidence: 99%

Image segmentation with a parametric deformable model using shape and appearance priors

El-Baz

Gimel’farb

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

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“…Traditionally, tubular binary mask is often created by a segmentation algorithm, e.g., [18,1,22,17,23]. and their centerline models are extracted by shortest paths algorithms operating on this mask [6,1,9,4,13,7,9,19,21]. Alternatively, centerlines can be constructed directly from images by the use of vesselness [11,25] or medialness filters [3,2,24,16].…”

Section: Introductionmentioning

confidence: 99%