13-11-2012, 03:50 PM
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features
A New Supervised Method for Blood Vessel.pdf (Size: 1.78 MB / Downloads: 58)
INTRODUCTION
DIABETIC retinopathy (DR) is the leading ophthalmic
pathological cause of blindness among people of working
age in developed countries [1]. It is provoked by diabetes-mellitus
complications and, although diabetes affection does not
necessarily involve vision impairment, about 2% of the patients
affected by this disorder are blind and 10% undergo vision
degradation after 15 years of diabetes [2], [3] as a consequence
of DR complications. The estimated prevalence of diabetes for
all age groups worldwide was 2.8% in 2000 and 4.4% in 2030,
meaning that the total number of diabetes patients is forecasted
to rise from 171 million in 2000 to 366 million in 2030 [4].
STATE OF ART
Many methods for retinal vessel segmentation have been
reported. These can be divided into two groups: rule-based
methods and supervised methods. In the first group, we highlight
methods using vessel tracking, mathematical morphology,
matched filtering, model-based locally adaptive thresholding or
deformable models. On the other hand, supervised methods are
those based on pixel classification (implementing some kind of
classifier).
Regarding rule-based methods, vessel tracking methods
[28]–[33] attempt to obtain the vasculature structure by following
vessel center lines. Starting from an initial set of points
established automatically or by manual labeling, vessels are
traced by deciding from local information the most appropriate
candidate pixel from those close to that currently under
evaluation. Other methods use mathematical morphology [15],
[34]–[36] to benefit from a priori-known vasculature shape
features, such as being piecewise linear and connected.
EXPERIMENTAL RESULTS
Performance Measures
In order to quantify the algorithmic performance of the proposed
method on a fundus image, the resulting segmentation is
compared to its corresponding gold-standard image. This image
is obtained by manual creation of a vessel mask in which all
vessel pixels are set to one and all nonvessel pixels are set to
zero. Thus, automated vessel segmentation performance can be
assessed.