Robust modelling of local image structures and its application to medical imagery

Author

Wang, Li ; Bhalerao, Abhir ; Wilson, Roland

Author_Institution

Dept. of Comput. Sci., Warwick Univ., Coventry, UK

Volume

3

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

534

Abstract

A robust modelling method for detecting and measuring isotropic, linear features and bifurcations is described and applied to analysing 2D electrophoresis and retinal images. Features are modelled as a superposition of Gaussian functions with the Hermite expansion and estimated by a combination of a multiresolution, windowed Fourier approach followed by an EM type of spatial regression. A penalised likelihood test, the Akakie information criteria (AIC) is used to select the best model and scale for feature segments. Results are shown by using samples on both gel and retinal images.

Keywords

Fourier transforms; Gaussian processes; approximation theory; electrophoresis; eye; feature extraction; image resolution; image sampling; image segmentation; maximum likelihood estimation; medical image processing; optimisation; regression analysis; 2D electrophoresis analysis; Akakie information criteria; EM algorithm; Gaussian function; Hermite approximation; Hermite expansion; bifurcation; expectation maximization algorithm; feature measurement; feature segmentation; image structure modeling method; isotropic linear feature detection; medical imagery; penalised likelihood test; retinal image analysis; robust modelling method; spatial regression analysis; windowed Fourier method; Application software; Bifurcation; Biomedical imaging; Computer vision; Image analysis; Image segmentation; Retina; Robustness; Signal resolution; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334584

Filename

1334584