Vascular tree segmentation in MRA images using Hessian-based multiscale filtering and local entropy thresholding

Hibet-Allah, Ouazaa; Jlassi, Hajer; Hamrouni, Kamel

doi:10.1109/atsip.2016.7523100

Cited by 3 publications

(1 citation statement)

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“…Yang et al [7] proposed an improved Frangi's method for preprocessing of coronary artery segmentation by adding geometric information obtained from ray-casting in a local sphere, in order to discriminate between the false step-edge responses at heart chamber and the positive vessel responses. Now, such vesselness information for quantifying the likeliness of each voxel to belong to a vascular or nonvascular, is often used for a initialization of other methods such as level-set approach [8] or local entropy approach [9].…”

Section: Related Workmentioning

confidence: 99%

Intracranial Vascular Structure Extraction: A Machine Learning Approach

Zhao

Wang

Xie³

et al. 2019

IEEE Access

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Extraction of brain blood vessels is an important issue for clinical assessment of intracranial vascular diseases. In this paper, the vessel extraction problem is formulated to a connected region classification problem. In processing images, an improved multi-scale filtering method is performed to improve vessel connectivity, and a post-processing step is added to harvest salient vessel candidates (SVC). Then, SVC is decomposed into connected regions and features are calculated and fed into a neural network classifier for training. For extraction, each connected region is individually analyzed using the trained neural network by considering the values of neighboring voxels that belong to its connection. The extraction results demonstrate the validity of the proposed approach.

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Section: Related Workmentioning

confidence: 99%