Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

Authors:
I. K. E. Purnama;K. Y. E. Aryanto;M. H. F. Wilkinson
Affiliations:
Sepuluh Nopember Institute of Technology ITS, Indonesia;Universitas Pendidikan Ganesha, Indonesia;University of Groningen, The Netherlands
Venue:
International Journal of E-Health and Medical Communications
Year:
2010

Citing 8
Cited 0

Attribute openings, thinnings, and granulometries

Computer Vision and Image Understanding
Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Preserving Filament Enhancement Filtering

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Automatic Hybrid Method for Retinal Blood Vessel Extraction

International Journal of Applied Mathematics and Computer Science - Selected Problems of Computer Science and Control
Antiextensive connected operators for image and sequence processing

IEEE Transactions on Image Processing
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation

IEEE Transactions on Image Processing
Volumetric Attribute Filtering and Interactive Visualization Using the Max-Tree Representation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.