Medical images compression by neural networks

Author

Benamrane, N. ; Dah, Z. Benahmed ; Shen, J.

Author_Institution

Dept. of Comput. Sci., USTO, Oran, Algeria

Volume

2

fYear

2003

fDate

18-20 Sept. 2003

Firstpage

1082

Abstract

This paper presents a compression method for still images, based on Kohonen´s neural network. To avoid the edge degradation caused by high compression ratio, the blocks are classified into two classes : blocks with high activity (edge blocks) and blocs with low activity. The image is divided first into blocks of 16 pixels. Each block of high activity are divided again into small blocks of 4 pixels. Blocks of high and low activity are coded separately with different codebooks. We have obtained a noticeable improvement of visual quality of all the rebuild images while keeping an important compression rate. This method has been tested on medical images.

Keywords

data compression; image coding; medical image processing; self-organising feature maps; Kohonens neural network; codebooks; high activity blocks; low activity blocks; medical image compression; visual quality improvement; Biomedical imaging; Computer science; Degradation; Electronic mail; Image coding; Image storage; Laboratories; Neural networks; Neurons; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on

Print_ISBN

953-184-061-X

Type

conf

DOI

10.1109/ISPA.2003.1296462

Filename

1296462