Retinal Image Enhancement and Automatic Retinal Structure Identification
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Automatic Retinal Vasculature Structure Tracing and
Vascular Landmark Extraction from Human Eye Image
Eunhwa Jung Kyungho Hong
Baekseok University, Korea;
This paper appears in: Hybrid Information Technology, 2006. ICHIT'06. Vol 2.
International Conference on
Publication Date: Nov. 2006
Volume: 2, On page(s): 161-167
Location: Cheju Island, Korea,
ISBN: 0-7695-2674-8
Digital Object Identifier: 10.1109/ICHIT.2006.253606
Posted online: 2006-12-11 09:16:15.0
Abstract
We present an effective algorithm for automatic tracing of vasculature structures and
vascular landmark extraction of bifurcations and ending points. In this paper we deal with
vascular patterns from digital images for personal identification. Vessel tracing
algorithms are of interest in a variety of biometric and medical application such as
personal identification, biometrics, and ophthalmic disorders like vessel change
detection. However eye surface vasculature tracing has many problems which are
subject to improper illumination, glare, fade-out, shadow and artifacts arising from
reflection, refraction, and dispersion. The proposed algorithm on vascular tracing
employs multi-stage processing of ten-layers as followings: Image Acquisition, Image
Enhancement by gray scale retinal image enhancement, reducing background artifact
and illuminations and removing interlacing minute characteristics of vessels, Vascular
Structure Extraction by connecting broken vessels, extracting vascular structure using
eight directional information, and extracting retinal vascular structure. Vascular
Landmark Extraction of bifurcations and ending points. The results of automatic retinal
vessel extraction using five different thresholds applied 34 eye images are presented.
The results of vasculature tracing algorithm shows that the suggested algorithm can
obtain not only robust and accurate vessel tracing but also vascular landmarks
according to thresholds.
http://www.springerlink.com/content/t21ulfhytdrvyq1a/
Automatic Detection of Microaneurysms in Color Fundus
Images of the Human Retina by Means of the Bounding Box
Closing
Book Series
Lecture Notes in Computer Science
Publisher
Springer Berlin / Heidelberg
ISSN
0302-9743
Subject
Computer Science
Volume
Volume 2526/2002
Book
Medical Data Analysis Third International :
Third International Symposium, ISMDA 2002
Rome, Italy, October 10-11, 2002. Proceedings
Copyright
2002
Pages
210-220
SpringerLink Date
Thursday, February 19, 2004
Authors
Thomas Walter, Jean-Claude Klein
Abstract
In this paper we propose a new algorithm for
the detection of microaneurysms in color
fundus images of the human retina.
Microaneurysms are the first unequivocal
indication of Diabetic Retinopathy (DR), a
severe and wide-spread eye disease. Their
automatic detection may play a major role in
computer assisted diagnosis of DR. We propose
an algorithm that can be divided into four
steps. The first step is an image enhancement
technique that comprises normalization and
noise reduction. The second step ist the
extraction of small details that fulfill a certain
criterion: This leads to the definition of the
bounding box closing. Then, an automatic
threshold depending on image quality is
calculated. In the last step false positives are
eliminated.
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http://adsabs.harvard.edu/abs/2006SPIE.6144.2080B
Retinal image enhancement
based on the human visual
system
Authors:
Belkacem-Boussaid, Kamel; Raman, Balaji;
Zamora, Gilberto; Srinivasan, Yeshwanth;
Bursell, Sven-Erick
Abstract
Improving the quality of gray level images continues to be a challenging task, and the
challenge increases for color images due to the interaction of multiple parameters within
a scene. Each color plane or wavelength constitutes an image by itself, and its quality
depends on many parameters such as absorption, reflectance or scattering of the object
with the lighting source. Non-uniformity of the lighting, optics, electronics of the camera,
and even the environment of the object are sources of degradation in the image.
Therefore, segmentation and interpretation of the image may become very difficult if its
quality is not enhanced. The main goal of the present work is to demonstrate image
processing algorithm that is inspired from some concepts of the Human Visual System
(HVS). HVS concepts have been widely used in gray level image enhancement and here
we show how they can be successfully extended to color images. The resulting Multi-
Scale Spatial Decomposition (MSSD) is employed to enhance the quality of color
images. Of particular interest for medical imaging is the enhancement of retinal images
whose quality is extremely sensitive to imaging artifacts. We show that our MSSD
algorithm improves the readability and gradeability of retinal images and quantify such
improvements using both subjective and objective metrics of image quality.