.png){kind=icon}
.png){kind=icon}
Return
Axis-guided patch based accurate segmentation for pathological vessels using adaptive weight sparse representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2019.101817.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background and objective: Over decades of research and development in vessel segmentation, accurate and reliable methods targeting at pathological vessels are few. Addressing this challenge, we try to delineate the vessel boundary accurately with the presence of pathologies. Methods: A novel segmentation framework is presented in this work, a vessel axis tracking algorithm + a patch-based sparse representation. The patch-based algorithm is navigated by the vessel axis tracking algorithm. Within the training process, multi-scale training samples have been used, which has the potential to be both physiologically accurate and computationally effective. The redundant information embedded in the multi-scale training samples is employed to delineate the pathological vessel accurately. To further reduce the computational burden caused by the patch-based sparse representation, a multi-scale dictionary has been generated, and adaptive weights have been assigned to the scale-specific sub-dictionary atoms, for computational efficiency. Results: Our method is evaluated by comparing with two state-of-the-art methods, on synthetic complex-structured datasets and real clinical datasets. The performance of our method is promising over the evaluation, since the overlap ratios of our method are high over all the datasets, around 91%, much better than two state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Patch-based vessel segmentation
Adaptive weight sparse representation
Multi-scale training samples
Pathological vessel
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.9
Papers:
9.8K
Citations:
2.4W
Organization
View more organizations