The document describes a method for glaucoma screening using retinal fundus images. Glaucoma is an irreversible eye disease that can cause vision loss if not detected early. The proposed method uses a novel sparse dissimilarity-constrained coding approach to segment and reconstruct the optic disc from fundus images. Reconstruction coefficients are used to calculate the cup to disc ratio, a metric for detecting glaucoma. The method was tested on 650 images and achieved better accuracy than other methods, with an average error of 0.064 compared to manual measurements. It also achieved good performance in glaucoma screening tests on two datasets. The method shows potential for large-scale population-based glaucoma screening using low-cost retinal imaging.

1. Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Title: Sparse Dissimilarity-Constrained Coding for
Glaucoma Screening
Abstract—Objective: Glaucoma is an irreversible chronic eye disease that leads to vision
loss. As it can be slowed down through treatment, detecting the disease in time is
important. However, many patients are unaware of the disease because it progresses
slowly without easily noticeable symptoms. Currently, there is no effectivemethod for
low-cost population-based glaucoma detection or screening. Recent studies have shown
that automated optic nerve head assessment from 2-D retinal fundus images is promising
for low-cost glaucoma screening. In this paper, we propose a method for cup to disc ratio
(CDR) assessment using 2-D retinal fundus images. Methods: In the proposed method,
the optic disc is first segmented and reconstructed using a novel sparse
dissimilarityconstrained coding (SDC) approach which considers both the
dissimilarityconstraint and the sparsity constraint from a set of reference discs with
known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to
compute the CDR for the testing disc. Results: The proposed method has been tested for
CDR assessment in a database of 650 images with CDRs manually measured by trained
professionals previously. Experimental results show an average CDR error of 0.064 and
correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-
the-art methods. Our proposed method has also been tested for glaucoma screening. The
method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images,
respectively, outperforming other methods. Conclusion: The proposed method achieves
good accuracy for glaucoma detection. Significance: The method has a great potential to
be used for large-scale population-based glaucoma screening.

2. Software & Hardware requirement:
1. Hardware requirement:
1. PC
2. RAM minimum 2GB
3. HDD minimum 100GB
2. Software requirement:
1. MATLAB 7.0
2. Signal processing toolbox
3. Image processing toolbox
4. Mathematical toolbox
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper

3. Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement
(Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com