DocumentCode
406592
Title
The generation of feature map in high dimensional feature space
Author
He, Renjie ; Narayana, Ponnada A.
Author_Institution
Medical Sch., Texas Univ., Houston, TX, USA
Volume
1
fYear
2003
fDate
17-21 Sept. 2003
Firstpage
649
Abstract
A method for generating feature maps in high dimensional (>4) feature space for tissue segmentation based on K-nearest neighbor (KNN) classification is presented. This technique considerably reduces the computational and memory complexity that are associated with the analysis of high dimensional feature space. This method has been successfully applied for segmenting MR images, based on four echoes, of multiple sclerosis brains.
Keywords
biomedical MRI; brain; image segmentation; medical image processing; K-nearest neighbor classification; MR images; echoes; feature map generation; high dimensional feature space; sclerosis brains; tissue segmentation; Biomedical imaging; Computational complexity; Helium; Hypercubes; Image segmentation; Magnetic resonance imaging; Multiple sclerosis; Partitioning algorithms; Prototypes; Solids;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE
ISSN
1094-687X
Print_ISBN
0-7803-7789-3
Type
conf
DOI
10.1109/IEMBS.2003.1279842
Filename
1279842
Link To Document