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The University of Melbourne, Australia

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3 Papers

A NEW AND EFFICIENT VESSEL SEGMENTATION METHOD FROM COLOR RETINAL IMAGES

Topics: Image Analysis and Processing; Detection and Identification

Authors: Alauddin Bhuiyan ¹ , Ryo Kawasaki ¹ , Ecosse Lamoureux ² , Kotagiri Ramamohanarao ¹ and Tien Y. Wong ²

Affiliations: ¹ The University of Melbourne, Australia , ² The University of Melbourne and National University of Singapore, Australia

Digital Object Identifier: 10.5220/0003161404670471

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

Retinal blood vessel changes (e.g., vessel caliber) are important indicators for earlier diagnosis of cardiovascular diseases. To quantify the changes automatically, a reliable vessel detection is essential. However, blood vessel detection in retinal image is complicated by a huge variation in a number of factors such as local contrast, vessel width and vessel central reflex. In this paper, we propose a new technique to detect retinal blood vessels which is able to address these issues. The core of the technique is a new vessel edge selection method which combines the method of finding edge pattern and edge profiling techniques. Experimental results show that 92.40% success rate in the identification of vessel start-points and 88.73% success rate in tracking the major vessels.

Related Ontology Subjects/Areas/Topics (6)

Applications and Services		Biomedical Signal Processing		Detection and Identification
Biomedical Engineering		Computer Vision, Visualization and Computer Graphics		Medical Image Detection, Acquisition, Analysis and Processing

AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING USING VASCULAR FEATURES

Topics: Biometrics; Pattern Recognition

Authors: Alauddin Bhuiyan , Baikunth Nath , Kotagiri Ramamohanarao and Tien Y. Wong

Affiliation: The University of Melbourne Australia, Australia

Digital Object Identifier: 10.5220/0003275903420347

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

In this paper, we propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image and a feature vector is constructed for each of the vessel-segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the vessel-segments actual hierarchical positions. Using these feature vectors, corresponding images are matched. An image matching method is demonstrated which identifies the same vessel in the corresponding images for comparing the desired feature(s). Initial results demonstrate that the method is suitable for image matching and patient longitudinal study.

Related Ontology Subjects/Areas/Topics (19)

Applications	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Artificial Intelligence	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Methodologies and Methods	Physiological Computing Systems
Biomedical Signal Processing	Multimedia	Sensor Networks
Biometrics	Multimedia Signal Processing	Soft Computing
Biometrics and Pattern Recognition	Neurocomputing	Telecommunications
Data Manipulation

RETINAL VASCULAR NETWORK MODEL - An Automatic Approach

Topic: Medical Image Detection, Acquisition, Analysis and Processing

Authors: Alauddin Bhuiyan , Baikunth Nath and Rao Kotagiri

Affiliation: The University of Melbourne, Australia

Digital Object Identifier: 10.5220/0003161602330238

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

In this paper, we propose a retinal vascular network model, which is an automatic process of generating a graph representation (i.e., a tree) of the retinal blood vessels and includes vessel geometrical features. It maps the retinal blood vessels and can facilitate vascular features such as the vessel width, bifurcation angle, among others to predict or earlier diagnose cardiovascular and related diseases. The proposed tree-model is based on vessel’s centerline, cross-sectional width, and bifurcation, branching and crossover points. The optic disc center is computed using the Hough transformation and vessel centerlines are tracked from out side its radius. Blood vessels are fragmented as vessel-segments based on the bifurcation, branching and crossover points. For each blood vessel we construct a binary tree which is linked in the root of the tree-model. Our automated method achieves an accuracy of 91.23% in extracting the vessel-segments.

Related Ontology Subjects/Areas/Topics (5)

Applications and Services		Biomedical Signal Processing		Medical Image Detection, Acquisition, Analysis and Processing
Biomedical Engineering		Computer Vision, Visualization and Computer Graphics