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Image Enhancement & Automatic Detection of Exudates in Diabetic Retinopathy
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Diabetic retinopathy (DR) is becoming a global health concern, which causes the loss of vision of most patients with the disease. Due to the vast prevalence of the disease, the automated detection of the DR is needed for quick diagnoses where the progress of the disease is monitored by detection of exudates changes and their classifications in the fundus retina images. Today in the automated system of the disease diagnoses, several image enhancement methods are used on original Fundus images. The primary goal of this thesis is to make a comparison of three of popular enhancement methods of the Mahalanobis Distance (MD), the Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE). By quantifying the comparison in the aspect of the ability to detect and classify exudates, the best of the three enhancement methods is implemented to detect and classify soft and hard exudates. A graphical user interface is also adopted, with the help of MATLAB. The results showed that the MD enhancement method yielded better results in enhancement of the digital images compared to the HE and the CLAHE. The technique also enabled this study to successfully classify exudates into hard and soft exudates classification. Generally, the research concluded that the method that was suggested yielded the best results regarding the detection of the exudates; its classification and management can be suggested to the doctors and the ophthalmologists.

Place, publisher, year, edition, pages
2019. , p. 54
Keywords [en]
Exudates, Diabetic Retinopathy, Mahalanobis Distance, Histogram Equalization, Contrast Limited Adaptive Histogram Equalization.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-18109OAI: oai:DiVA.org:bth-18109DiVA, id: diva2:1327151
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Examiners
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved

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CiteExportLink to record
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  • apa
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