TY  - JOUR
T1  - Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images.
JF  - IEEE Transactions on information technology in biomedicine
A1  - Chia-Ling Tsai
LA  - English
UL  - https://tuklas.up.edu.ph/Record/UP-99796217609548011
AB  - A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2° down to 3.8°.
KW  - Accurate morphometry.
KW  - Biomedical image analysis.
KW  - Crossovers.
KW  - Exclusion region and position refinement algorithm.
KW  - Feature extraction.
KW  - Feature refinement.
KW  - Feature stability.
KW  - Human retinal images.
KW  - Image registration.
KW  - Landmarks.
KW  - Location repeatability.
KW  - Median location error.
KW  - Model-based algorithm.
KW  - Mosaic synthesis.
KW  - Neuronal structure.
KW  - Vascular bifurcation.
KW  - Vessel orientation.
ER  -