A novel method for glaucoma detection using optic disc and cup segmentation in digital retinal fundus images

Jose, Asha Merin; Balakrishnan, Arun A.

doi:10.1109/iccpct.2015.7159441

Cited by 20 publications

(13 citation statements)

References 12 publications

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“…In [54], authors estimated OD center using thresholding and distance transformation technique, which was then followed by the estimation of Eigenvector spaces of normal set and glaucoma using PCA algorithm. As classifier, authors [54] applied different algorithms like Bayes classifiers [54], K-NN [50], logistic regression [55]. Authors [55] applied GLCM with linear transformation technique to perform Glaucoma detection.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

2019

IJITEE

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“…However, its performance can't be generalized as the aforesaid trait might vary over one to another. In [36][37], a self assessed disc segmentation method was proposed for OD and OC segmentation [44], where deriving CDR values in the fundus image authors performed Glaucoma detection. However, the use of static CDR threshold for classification limits its generalization.…”

Section: Related Workmentioning

confidence: 99%

GlaucoNet: A Highly Robust Stacked Auto Encoder assisted Deep Learning Model for Glaucoma Detection System

Patil*,

Rao

2019

IJEAT

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High pace rise in Glaucoma, an irreversible eye disease that deteriorates vision capacity of human has alarmed academia-industries to develop a novel and robust Computer Aided Diagnosis (CAD) system for early Glaucomatic eye detection. The main root cause for glaucoma growth depends on its structural alterations in the retina and is very much essential for ophthalmologists to identify it at an initial period to stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Recently, numerous efforts have been made to exploit SpatialTemporal features including morphological values of Optical Disk (OD), Optical Cup (OC), Neuro-Retinal Rim (NRR) etc to perform Glaucoma detection in fundus images. Here, some issues like: suitable pre-processing, precise Region of Interest segmentation, post-segmentation and lack of generalized threshold limits efficacy of the major existing approaches. Furthermore, the optimal segmentation of OD and OC, nerves removal from OD or OC is often tedious and demands more efficient solution. However, these approaches cumulatively turn out to be computationally complex and time-consuming. As potential alternative, deep learning techniques have gained widespread attention, especially for image analysis or vision technologies. With this motive, in this paper, the authors proposed a novel Convolutional Stacked Auto-Encoder (CSAE) assisted Deep Learning Model for Glaucoma Detection and Classification model named GlaucoNet. Unlike classical methods, GlaucoNet applies Stacked Auto-Encoder by using hierarchical CNN structure to perform deep feature extraction and learning. By adapting complex data nature, and large features, GlaucoNet was designed with three layers: convolutional layer (CONV), Max-pool layer (MP) and two Fully Connected (FC) layers where the first performs feature extraction and learning, while second exhibits feature selection followed by the reduction of spatial resolution of the individual feature map to avoid large number of parameters and computational complexities. To avoid saturation problem in this work, by marking an applied dropout as 0.5. MATLAB based simulation-results with DRISHTI-GS and DRION-DB datasets affirmed that the proposed GlaucoNet model outperforms as compared to other state-of-art techniques: neural network based approaches in terms of accuracy, recall, precision, F-Measure and balanced accuracy. The overall parametric measured values shown better performance for GlaucoNet model.

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“…O método proposto é testado em 50 imagens, coletadas em um banco de dados, e encontrou com sucesso as amostras doentes em 48 casos com a taxa de sucesso de 96%. Jose and Balakrishnan (2015) utilizam a segmentação das regiões do disco óptico e da escavação na sua metodologia. A região do DO é segmentada usando operações morfológicas e uma metodologia híbrida.…”

Section: Morfologia Matemáticaunclassified

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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A novel method for glaucoma detection using optic disc and cup segmentation in digital retinal fundus images

Cited by 20 publications

References 12 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+

GlaucoNet: A Highly Robust Stacked Auto Encoder assisted Deep Learning Model for Glaucoma Detection System

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

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