An improved seeded region growing algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thin Network Extraction in 3D Images: Application to Medical Angiograms
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Blood vessel segmentation using multi-scale quadrature filtering
Pattern Recognition Letters
International Journal of Bioinformatics Research and Applications
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Three-dimensional (3D) segmentation of blood vessels plays a very important role in solving some practical problems such as diagnosis of vessels diseases. Because of the effective segmentation for 2D images, the fuzzy connectedness segmentation method is introduced to extract vascular structures from 3D blood vessel volume dataset. In the experiments, three segmentation methods including thresholding method, region growing method and fuzzy connectedness method are all used to extract the vascular structures, and their results are compared. The results indicate that fuzzy connectedness method is better than thresholding method in connectivity of segmentation results, and better than region growing method in precision of segmentation results.