Title :
Segmentation of vessel images using a localized hybrid level-set method
Author :
Qingqi Hong ; Beizhan Wang
Author_Institution :
Software Sch., Xiamen Univ., Xiamen, China
Abstract :
In this paper, a localized hybrid level-set method for vessel image segmentation is proposed. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. In our proposed technique, the local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the global threshold based method, the use of locally specified dynamic thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Experimental results on vessel images are presented to demonstrate the strengths of our localized hybrid level-set method.
Keywords :
differential geometry; feature extraction; image segmentation; medical image processing; boundary information; geodesic active contour model; local intensity information; local region information; localized hybrid level-set method; region-based contour model; vessel image segmentation; Active contours; Biomedical imaging; Computational modeling; Data mining; Deformable models; Geometry; Image segmentation; level-set; segmentation; vessel images;
Conference_Titel :
Image and Signal Processing (CISP), 2013 6th International Congress on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-2763-0
DOI :
10.1109/CISP.2013.6745243