期刊
MULTIMEDIA SYSTEMS
卷 25, 期 2, 页码 109-118出版社
SPRINGER
DOI: 10.1007/s00530-017-0580-7
关键词
Blood vessel; Segmentation; Rule-based; Machine learning; Deep neural network
资金
- National Natural Science Foundation of China [61601363, 61372046, 61640418, 61401264, 11571012, 81530058, 61601154, 61502387]
- National Key R&D Program of China [2016YFC1300300]
- Science and Technology Plan Program in Shaanxi Province of China [2013K12-20-12, 2015KW-002]
- Natural Science Research Plan Program in Shaanxi Province of China [2017JQ6017, 2015JM6322, 2015JZ019]
- Scientific Research Foundation of Northwest University
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据