By Topic

Magnetic resonance image segmentation using optimized nearest neighbor classifiers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hong Yan ; Dept. of Electr. Eng., Sydney Univ., NSW, Australia ; Jingtong Mao ; Yan Zhu ; Chen, B.

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

Published in:

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference  (Volume:3 )

Date of Conference:

13-16 Nov 1994