Title :
Vessel extraction techniques and algorithms: a survey
Author :
Kirbas, Cemil ; Quek, Francis K H
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
Abstract :
Vessel segmentation algorithms are critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms, putting the various approaches and techniques in perspective by means of a classification of the existing research. While we target mainly the extraction of blood vessels, neurovascular structure in particular we also review some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We divide vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) miscellaneous tube-like object detection approaches. Some of these categories are further divided into sub-categories. A table compares the papers against such criteria as dimensionality, input type, preprocessing, user interaction, and result type.
Keywords :
artificial intelligence; blood vessels; diagnostic radiography; feature extraction; image recognition; medical image processing; neural nets; object detection; physiological models; reviews; algorithms; artificial intelligence-based approaches; blood vessel delineation; blood vessels; circulatory blood vessel analysis systems; dimensionality; image segmentation methods; input type; malformations; medical images; model-based approaches; neural network-based approaches; neurovascular structure; patient diagnosis; pattern recognition techniques; preprocessing; stenosis; tracking-based approaches; tube-like object detection approaches; user interaction; vessel diagnosis; vessel image extraction techniques; Artificial intelligence; Artificial neural networks; Biomedical imaging; Blood vessels; Image resolution; Image segmentation; Intelligent networks; Medical diagnostic imaging; Object detection; Pattern recognition;
Conference_Titel :
Bioinformatics and Bioengineering, 2003. Proceedings. Third IEEE Symposium on
Print_ISBN :
0-7695-1907-5
DOI :
10.1109/BIBE.2003.1188957