Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma

Lotankar, Megha L.; Noronha, Kevin; Koti, Jayasudha

doi:10.1109/upcon.2015.7456741

Cited by 28 publications

(18 citation statements)

References 14 publications

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“…Table III and Table IV discussed performance of the proposed GlaucoNet+ model with different classification setups (Table III) and configuration (Table IV); however to further examine efficacy of our proposed model in comparison to the other existing glaucoma detection and classification methods, we have performed analysis based on secondary resources (reviewing existing methods or allied papers). [49] 84.38 --- [54] 95.50 --- [60] --- [64] 88.00 --- [35] --98.60 - [36] 99.20 -86.00 - [65] ---- [47] 80.00 -95.00 - [48] 97.00 --- [57] 98 --- [39] 94.10 -91.80 - [75] 90.00 --- [40] --92.00 - [33] 100.00 -94.00 - [59] ---- [66] 89.6 (NB) 97.6(AN N) --- [67] 92.00 --- [68] ---- [69] 72.38 --- [70] 79.00 -87.00 - [71] 83.10 --- [62] 93.00 --- [72] 96.67 -100.00 - [73] 91.00 --- [74] 92.00 --- [4] 0.8478 - [55] 88. Observing the results, it can be found that the proposed GlaucoNet+ model with Hybrid feature extraction and SVM (polynomial) with 10-fold cross validation outperforms major existing approaches.…”

Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

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The exponential rise in technologies has revitalized academia-industries to achieve more efficient computer aided diagnosis systems. It becomes inevitable especially for Glaucoma detection which has been increasing with vast pace globally. Most of the existing approaches employs morphological features like optical disk and optical cup information, optical cup to disk ratio etc; however enabling optimal detection of such traits has always been challenge for researchers. On the other hand, in the last few years deep learning methods have gained widespread attention due to its ability to exploit fine grained features of images to make optimal classification decision. However, reliance of such methods predominantly depends on the presence of deep features demanding suitable feature extraction method. To achieve it major existing approaches extracts full-image features that with high dimensional kernel generates gigantically huge features, making classification computationally overburdened. Therefore, retaining optimal balance between deep features and computational overhead is of utmost significance for glaucoma detection and classification. With this motive, in this paper a novel hybrid deep learning model has been developed for Glaucoma detection and classification. The proposed Hybrid CNN model embodies Stacked Auto-Encoder (SAE) with transferable learning model AlexNet that extracts high dimensional features to make further two-class classification. To achieve computational efficiency, In addition to the classical ReLu and dropout (50%), we used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms. We applied 10-fold cross validation assisted Support Vector Machine classifier to perform two-class classification; Glaucomatous and Normal fundus images. Simulation results affirmed that the proposed Hybrid deep learning model with LDA feature selection and SVM-Poly classification achieves the maximum accuracy of 98.8%, precision 97.5%, recall 97.5% and F-Measure of 97.8%.

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Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

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“…A ideia básica é começar com uma curva inicial e depois deformar essa curva para o limite do objeto que se deseja segmentar. Essa deformação ocorre sob algumas restrições da imagem (Lotankar et al;2015). Joshi et al (2011) propuseram um método para a segmentação do disco óptico e da escavação que se baseia em um modelo de contorno ativo e em evidências anatômicas, tais como curvas de vasos.…”

Section: Contorno Ativounclassified

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

Claro

Veras

Santos

et al. 2018

RBCA

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A utilização de técnicas de processamento digital de imagens (PDI) é destaque no cenário médico para o diagnóstico automático de patologias. Na área oftalmológica o glaucoma é a segunda principal causa da perda de visão no mundo e não possui cura. Atualmente, existem tratamentos para prevenir a perda da visão, contudo a doença deve ser descoberta nos estágios iniciais. O objetivo principal deste artigo é revisar as metodologias e técnicas de segmentação dos limites do disco óptico e escavação. Essas regiões são utilizadas para o cálculo de métricas para classi cação do glaucoma e auxílio aos pro ssionais da área. Os trabalhos mais recentes publicados na área foram classi cados em cinco grupos de acordo com a principal técnica de PDI aplicada: agrupamento, superpixel, contorno ativo, morfologia matemática e redes neurais convolucionais. Além disso, foi realizado um levantamento das principais bases de imagens e métricas de avaliação utilizadas.Palavras-Chave: Algoritmos de Agrupamento; Contorno Ativo; Glaucoma; Morfologia matemática; Redes neurais convolucionais; Superpixel. Abstract The use of digital image processing techniques (DIP) is highlighted in the medical scenario for automatic diagnosis of pathologies. In the ophthalmologic area, glaucoma is the second leading cause of vision loss in the world

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“…The detection of optic disc and optic cup from colour retinal images for glaucoma detection is approached in [4]. An automatic system is developed for glaucoma detection by extracting various features like vertical CDR, Horizontal to Vertical CDR (H-V CDR), CDAR and Rim to Disc Area Ratio from digital fundus images through segmentation of Optic disc, cup and neuro retinal rim.…”

Section: Introductionmentioning

confidence: 99%

Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image

Maharaja¹,

Shaby²

2017

IJMSR

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Abstract:Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. There is a need to diagnose glaucoma accurately with low cost. In this paper, a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT) is proposed. The EWT is used to decompose the image and Gray Level Concurrence Matrix (GLCM) features are obtained from decomposed EWT components. Then, these features are used for the classification of normal and glaucoma images using Neural Network (NN) classifier. The evaluation of the system is carried on using 46 generated images from that 20 images are used for training purpose and 26 images are used for testing purpose. In this the classification rate of the proposed system is satisfied. Overall accuracy of the proposed system is 96%.

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Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma

Cited by 28 publications

References 14 publications

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image

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