Title :
Segmentation of MR osteosarcoma images
Author :
Pan, Jincheng ; Li, Minglu
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China
Abstract :
There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.
Keywords :
fuzzy set theory; image segmentation; magnetic resonance imaging; medical image processing; pattern clustering; pattern recognition; FCM; MR image segmentation; MR osteosarcoma image; fuzzy c-means clustering algorithm; multispectral segmentation; pattern recognition; single image; Biomedical imaging; Clustering algorithms; Computed tomography; Computer science; Data mining; Image edge detection; Image segmentation; Magnetic resonance imaging; Neoplasms; Pattern recognition;
Conference_Titel :
Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on
Print_ISBN :
0-7695-1957-1
DOI :
10.1109/ICCIMA.2003.1238155