DocumentCode :
2134289
Title :
Magnetic resonance image segmentation using optimized nearest neighbor classifiers
Author :
Yan, Hong ; Mao, Jingtong ; Zhu, Yan ; Chen, Benjamin
Author_Institution :
Dept. of Electr. Eng., Sydney Univ., NSW, Australia
Volume :
3
fYear :
1994
fDate :
13-16 Nov 1994
Firstpage :
49
Abstract :
The nearest neighbor rule has previously been shown to be the most reliable method for segmentation of at least a certain range of magnetic resonance images compared with other supervised learning techniques. A nearest neighbor classifier may require long computing time and large memory space if the number of prototypes used is large. The authors present a method for image segmentation using optimized nearest neighbor classifiers. In the method only a very small number of prototypes are generated from training samples using an unsupervised learning method. The prototypes are then optimized using a neural network based on supervised learning. The optimized nearest neighbor classifier is robust in performance for image segmentation and very efficient for practical implementation
Keywords :
biomedical NMR; brain; image classification; image segmentation; medical image processing; neural nets; optimisation; unsupervised learning; magnetic resonance image segmentation; neural network; optimized nearest neighbor classifiers; practical implementation; prototypes; supervised learning; training samples; unsupervised learning method; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Nearest neighbor searches; Pixel; Prototypes; Radiology; Robustness; Supervised learning; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
Type :
conf
DOI :
10.1109/ICIP.1994.413890
Filename :
413890
Link To Document :
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