DocumentCode
353355
Title
Medical image compression by “neural-gas” network and principal component analysis
Author
Meyer-Baese, A.
Author_Institution
High Speed Digital Archit. Lab., Florida Univ., Gainesville, FL, USA
Volume
5
fYear
2000
fDate
2000
Firstpage
489
Abstract
This paper presents a new compression scheme for digital still images, by using the “neural-gas” network for codebook design, and several linear and nonlinear principal component methods as a preprocessing technique. We investigate the performance of the compression scheme depending on the blocksize, codebook and number of chosen principal components. The nonlinear principal component method shows the best compression results in combination with the “neural-gas” network
Keywords
data compression; image coding; medical image processing; neural nets; principal component analysis; blocksize; codebook; image compression; medical image processing; neural-gas network; principal component analysis; still digital images; Biomedical imaging; Image coding; Image storage; Karhunen-Loeve transforms; Laboratories; Neural networks; Neurons; Nonlinear distortion; Principal component analysis; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861517
Filename
861517
Link To Document