Abstract:Retinal image analysis is one of the crucial topics in medical image processing. During the last three decades, people are trying to extract the different features (like blood vessels, optic disk, macula, fovea etc.) automatically from retinalimage. Fovea and optic disc are the important features of a fundus retinal image.The quantitative analysis of retinal images is of increasing importance in the diagnosis of the various eye disorders.Hence a number of algorithms and techniques are being developed for its segmentation. This paper analyses various algorithms and techniques proposed earlier for the detection of optic disc and fovea.
Retinal vasculature extraction is an area of utmost
interest in ophthalmology. It helps to diagnose various diseases
and also play a crucial role in treatment planning and
accomplishment.In this work, we suggest an algorithm to
segmentretinal vasculature fromretinal Fundus Images(FI) using
multi-structure element morphology after enhancing the image
using Normal Inverse Gaussian (NIG) model in the fuzzified
Non-Subsampled Contourlet Transform (NSCT) domain. Since
both noises and weak edges produce low magnitude NSCT
coefficients, image enhancement methods amplify weak edges as
well as noises. Direct application of image boosting technique in
the NSCT domain causes over enhancement. So a novel image
enhancement method is employed by interpreting the term
“contrast” as a qualitative instead of a quantitative measure of the
image. Membership values of NSCT coefficients are modified
using NIG model. Mathematical Morphology(MM) by
Multi-structure Elements (MEs) is used to extract the edges of
image. False vessel ridges are expunged, and the thin vessel edges
are preserved using opening by reconstruction. Connected
component analysis followed by length filtering is used to filter the
still remaining false edges. In most of the available literature,
low-resolution fundus image databases are used for evaluating the
algorithm. In our work, we evaluate our algorithm not only
utilizing the DRIVE database, a low-resolution retinal image (RI)
database, but also using an openly available High-Resolution
Fundus (HRF) image database. Our result illustrates that the
proposed method outperforms the other techniques considered
with average accuracy (ACC) of 96.71%. In addition to ACC, we
also use F1-Score and Mathews Correlation Coefficient (MCC) to
evaluate our method. The average values of the results obtained
with the HRF image database for F1-Score and MCC are 0.8172
and 0.8031, respectively, which are very much encouraging
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.