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Proceedings Paper

Feature extraction and segmentation in medical images by statistical optimization and point operation approaches
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Paper Abstract

Feature extraction is a critical preprocessing step, which influences the outcome of the entire process of developing significant metrics for medical image evaluation. The purpose of this paper is firstly to compare the effect of an optimized statistical feature extraction methodology to a well designed combination of point operations for feature extraction at the preprocessing stage of retinal images for developing useful diagnostic metrics for retinal diseases such as glaucoma and diabetic retinopathy. Segmentation of the extracted features allow us to investigate the effect of occlusion induced by these features on generating stereo disparity mapping and 3-D visualization of the optic cup/disc. Segmentation of blood vessels in the retina also has significant application in generating precise vessel diameter metrics in vascular diseases such as hypertension and diabetic retinopathy for monitoring progression of retinal diseases.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481154
Show Author Affiliations
Shuyu Yang, Texas Tech Univ. (United States)
Philip King, Texas Tech Univ. (United States)
Enrique Corona, Texas Tech Univ. (United States)
Mark P. Wilson, Texas Tech Univ. (United States)
Kestrel Corp. (United States)
Kaan Aydin, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
Peter Soliz, Kestrel Corp. (United States)
Brian S. Nutter, Texas Tech Univ. (United States)
Young H. Kwon, Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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