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15 May 2003 Feature extraction and segmentation in medical images by statistical optimization and point operation approaches
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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.
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Shuyu Yang, Philip King, Enrique Corona, Mark P. Wilson, Kaan Aydin, Sunanda Mitra, Peter Soliz, Brian S. Nutter, and Young H. Kwon "Feature extraction and segmentation in medical images by statistical optimization and point operation approaches", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481154
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