Authors:
Alauddin Bhuiyan
;
Baikunth Nath
;
Joselito Chua
and
Kotagiri Ramamohanarao
Affiliation:
The University of Melbourne, Australia
Keyword(s):
Microvascular Sign, Gradient Operator, Adaptive Region Growing Technique, Texture Classification, Gabor Energy Filter Bank, Fuzzy C-Means Clustering.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
Vessel diameter is an important factor for indicating retinal microvascular signs. In automated retinal image analysis, the measurement of vascular width is a complicated process as most of the vessels are few pixels wide. In this paper, we propose a new technique to measure the retinal blood vessel diameter which can be used to detect arteriolar narrowing, arteriovenous (AV) nicking, branching coefficients, etc. to diagnose related diseases. First, we apply the Adaptive Region Growing (ARG) segmentation technique to obtain the edges of the blood vessels. Following that we apply the unsupervised texture classification method to segment the blood vessels from where we obtain the vessel centreline. Then we utilize the edge image and vessel centreline image to obtain the potential pixels pairs which pass through a centreline pixel. We apply a rotational invariant mask to search the pixel pairs from the edge image. From those pixels we calculate the shortest distance pair which will be t
he vessel width for that cross-section. We evaluate our technique with manually measured width for different vessels’ cross-sectional area and achieve an average accuracy of 95.8%.
(More)