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Segmentation of blood vessels using rule-based and machine-learning-based methods: a review

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Abstract

Vessel segmentation as a component of medical image processing is the prerequisite for accurate diagnosis of vascular-related diseases. Manual delineation of blood vessels has been turned out to be time consuming and observer dependent. Therefore, much effort has been dedicated to the automatic or semi-automatic vessel segmentation methods. Previous literatures have reviewed the state of vessel segmentation methods from various perspectives. However, their reviews did not take the modern machine-learning methods especially deep neural networks into account. In this paper, we reviewed the state-of-the-art vessel segmentation methods by dividing them into two categories, rule-based, and machine-learning-based methods. The rule-based methods discriminate vessel structure from background relying on intuitively and exquisitely designed rule sets, while the machine-learning-based methods carry out the segmentation by self-learned rules from the previous experience. Instead of exhaustively listing all vessel segmentation methods, this paper focuses on the well-known blood vessel segmentation methods in recent years, to give readers a glimpse of the current state and future direction of segmentation technique for blood vessels.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61601363, 61372046, 61640418, 61401264, 11571012, 81530058, 61601154, and 61502387, the National Key R&D Program of China under Grant No. 2016YFC1300300, the Science and Technology Plan Program in Shaanxi Province of China under Grant Nos. 2013K12-20-12 and 2015KW-002, the Natural Science Research Plan Program in Shaanxi Province of China under Grant Nos. 2017JQ6017, 2015JM6322, and 2015JZ019, and the Scientific Research Foundation of Northwest University.

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    Fengjun Zhao, Yanrong Chen, Yuqing Hou & Xiaowei He

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Zhao, F., Chen, Y., Hou, Y. et al. Segmentation of blood vessels using rule-based and machine-learning-based methods: a review. Multimedia Systems 25, 109–118 (2019). https://doi.org/10.1007/s00530-017-0580-7

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