Segmentation of regions in JPEG compressed medical images

Author

Singh, Pramod K.

Author_Institution

Sch. of Comput. Sci. & Eng., New South Wales Univ., Sydney, NSW, Australia

Volume

5

fYear

2004

fDate

24-27 Oct. 2004

Firstpage

3483

Abstract

A novel algorithm for the segmentation of medical images using features derived directly from JPEG compressed domain is proposed in this paper. The algorithm uses features extracted from DCT coefficients without its inverse transform and the rule based fisher discriminant K-means (FDK) technique for clustering image pixels based on derived feature vectors. In this study, we extract features for each 2×2 DCT block of compressed image. The extracted feature vector is used by an extended version of the adaptive K-means clustering algorithm for the classification of image pixels.

Keywords

data compression; discrete cosine transforms; feature extraction; image classification; image coding; image resolution; image segmentation; DCT coefficient; JPEG compressed medical image; adaptive K-means clustering algorithm; discrete cosine transform; feature extraction; feature vector; fisher discriminant K-mean technique; image pixel clustering; image segmentation; inverse transform; Biomedical imaging; Classification algorithms; Clustering algorithms; Discrete cosine transforms; Feature extraction; Image coding; Image segmentation; Medical diagnostic imaging; Pixel; Transform coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2004. ICIP '04. 2004 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-8554-3

Type

conf

DOI

10.1109/ICIP.2004.1421865

Filename

1421865