Open AccessSegmentation of Image Using Watershed and Fast Level set methods
Author(s) -
Minal M. Purani,
Shobha Krishnan
Publication year - 2012
Publication title -
international journal of computer and communication technology
Language(s) - English
Resource type - Journals
eISSN - 2231-0371
pISSN - 0975-7449
DOI - 10.47893/ijcct.2012.1112
Subject(s) - fast marching method , watershed , computer vision , artificial intelligence , robustness (evolution) , computer science , image segmentation , level set method , segmentation , medical imaging , noise (video) , pattern recognition (psychology) , set (abstract data type) , level set (data structures) , image (mathematics) , programming language , biochemistry , chemistry , gene
Technology is proliferating. Many methods are used for medical imaging .The important methods used here are fast marching and level set in comparison with the watershed transform .Since watershed algorithm was applied to an image has over clusters in segmentation . Both methods are applied to segment the medical images. First, fast marching method is used to extract the rough contours. Then level set method is utilized to finely tune the initial boundary. Moreover, Traditional fast marching method was modified by the use of watershed transform. The method is feasible in medical imaging and deserves further research. It could be used to segment the white matter, brain tumor and other small and simple structured organs in CT and MR images. In the future, we will integrate level set method with statistical shape analysis to make it applicable to more kinds of medical images and have better robustness to noise.