Title :
Medical image compression by “neural-gas” network and principal component analysis
Author_Institution :
High Speed Digital Archit. Lab., Florida Univ., Gainesville, FL, USA
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861517
