Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier

One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a c...

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Bibliographic Details
Published in:BioMed research international 2018-01, Vol.2018 (2018), p.1-12
Main Authors: Chu, Dianhui, Ding, Deqiong, Cheng, Yuanzhi, Hu, Xin
Format: Article
Language:English
Subjects:
Algorithms
Arteries
Automation
Boundaries
Carotid arteries
Carotid Arteries - diagnostic imaging
Carotid artery
Classification
Classifiers
Datasets
Datasets as Topic
Eigenvalues
Humans
Machine learning
Medical imaging
Methods
Models, Biological
Segmentation
Support Vector Machine
Support vector machines
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Summary:One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.
ISSN:2314-6133
2314-6141
DOI:10.1155/2018/3636180