Fractal analysis of neovascularization due to diabetic retinopathy in retinal fundus images

Mudigonda, Sathvika; Oloumi, Faraz; Katta, Kalyana M.; Rangayyan, Rangaraj M.

doi:10.1109/ehb.2015.7391551

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…In [20], dynamic shape features of blood vessels were used for micro-aneurysm and hemorrhage detection. In [21], emphasis was made on detecting and classifying neovascularization originated owing to PDR. A technique called JSEG was used in [22] to detect exudates and abnormalities in a macular region.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

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The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)-and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR. Keywords Computer-aided diagnosis Á Diabetic retinopathy Á Deep neural network Á AlexNet DNN Á Convolutional neural network Á Gaussian mixture model Á Linear discriminant analysis Á SVM

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Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

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“…In [6] blood vessels are detected using image processing algorithms. Fractal analysis box counting measure is used to detect neovascularisation near optic disk.…”

Section: Related Workmentioning

confidence: 99%

A Machine Vision Based Detection & Classification of Neovascularisation in Retinal Blood Vessels

Sudha¹,

Srinivasan²,

Karthik³

et al. 2019

IJITEE

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Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. In this proposed system techniques are introduced to detect and classify neovascularisation. Input fundus image is preprocessed by median filtering and further new vessels are segmented by using Fuzzy c-means clustering algorithm. After segmentation SIFT features are extracted and are used to train support vector machine (SVM) classifier. This automated system has been tested for 70 fundus images and accuracy of 96% is achieved

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“…Regarding to retinal fundus images, fractals have only been applied to the retinal blood vessels characterization. In [11], a fractal analysis approach based on the boxcounting method was used to quantify vascular patterns in normal and abnormal cases showing neovascularization (caused by proliferative diabetic retinopathy in advanced stages of the disease). The algorithm was validated in ten images.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of fractal dimension effectiveness for damage detection in retinal background

Colomer

Naranjo

Janvier

et al. 2018

Journal of Computational and Applied Mathematics

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This work investigates the characterization of bright lesions in retinal fundus images using texture analysis techniques. Exudates and drusen are evidences of retinal damage in diabetic retinopathy (DR) and age-related macular degeneration (AMD) respectively. An automatic detection of pathological tissues could make possible an early detection of these diseases. In this work, fractal analysis is explored in order to discriminate between pathological and healthy retinal texture. After a deep preprocessing step, in which a spatial and colour normal

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Fractal analysis of neovascularization due to diabetic retinopathy in retinal fundus images

Cited by 15 publications

References 30 publications

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

A Machine Vision Based Detection & Classification of Neovascularisation in Retinal Blood Vessels

Evaluation of fractal dimension effectiveness for damage detection in retinal background

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