Dry and Wet Age-Related Macular Degeneration Classification

Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

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Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

Abstract:

The condition of the vascular network of the human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to the variable size of vessels, relatively low contrast, and the potential presence of pathologies like microaneurysms and hemorrhages. The Project proposes Retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. It plays important role in the detection of some diseases in the early stages, such as diabetes, which can be performed by comparing the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. The green channel will be selected for image analysis to extract vessels accurately. The Daubechies wavelet transform is used to enhance the image contrast for effective vessels detection. The directionality feature of the multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multi-structure elements. A simple thresholding method along opening and closing indicates the remained ridges belonging to vessels. The experimental result proves that the blood vessels and exudates can be effectively detected by applying this method on the retinal images. Dry and Wet Age-Related Macular Degeneration Classification

Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

Existing Method:

Edge detection methods
Segmentation like simple and global thresholding algorithm

Drawbacks:

In this method, intrinsic characteristics of retinal images make the blood vessel detection process difficult.
Poor Edge detection.
Not possible to cluster a fundus Exudates.?

Proposed System:

The combination of multi-structure morphological process and Segmentation technique is used effectively for retinal vessel and exudates detection.

Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

Block Diagram:

Modules:

Daubechies? Wavelet
GLCM Features
NN Classifier
Morphological Process

Advantages:

Accurate retina vessel and execute detection
It is useful in diabetic diagnosis

Applications:

Biomedical application for retinal image analysis and fundus, exudates detection.
Image Fusion of CT Machine.

Software Requirements:

MATLAB 2018a? and above versions

Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

REFERENCES:

[1] T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard,?Retinal arteriolar narrowing and risk of coronary heart disease in men and women: The atherosclerosis risk in communities study,? The Journal of the American Medical Association, vol. 287(9), pp. 1153? 9, 2002.?

[2] M. D. knudtson, K. E. Lee, L. D. Hubbard, T. Y. Wong, R. Klein, and B. E. Klein,?Is retinal photography useful in the measurement of stroke risk?? Lancet Neurol, vol. 3(3), pp. 179? 83, 2004.?

[3] W. H. O. Fact Sheet on Cardiovascular Diseases, http://www.who.int/ cardiovascular diseases/en/. (last accessed on 12 July, 2010), 2010.?

[4] M. Saeza, S. Gonzalez-Vazquezc, M. Gonzalez-Penedoc, M. A. Barcelo, M. Pena-Seijod, G. C. de Tuerob, and A. Pose-Reinod, ?Development of an automated system to classify retinal vessels into arteries and veins,? Computer methods and programs in biomedicine, vol. PMID: 22424729, pp. 1?10, 2012.?

[5] H. Azegrouz and E. Trucco, ?Max-min central vein detection in retinal fundus images,? In the proceedings of IEEE International Conference on Image Processing, 2006, vol. 10.1109/ICIP.2006.313145, pp. 1925 ? 1928, 2006.