The comparison of GVF Snake Active Contour method and Ellipse Fit in optic disc detection for glaucoma diagnosis

Kusumandari, Dwi Esti; ArisMunandar,; Redhyka, Grace Gita

doi:10.1109/icacomit.2015.7440189

Cited by 11 publications

(6 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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“…Nonetheless, the method may fail in poor-quality images. Kusumandari et al [24] presented a comparison of GVF snakes and the ellipse-fitting method in detecting the OD. They concluded that the GVF gives better accuracy than the ellipse-fitting method.…”

Section: Od Segmentationmentioning

confidence: 99%

Glaucoma Detection in Mobile Phone Retinal Images Based on ADI-GVF Segmentation with EM initialization

et al. 2021

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The advanced development of mobile phone and lens technology has made retinal imaging more convenient than ever before. In the digital health era, mobile phone fundus photography has evolved into a low-cost alternative to the standard ophthalmoscope. Existing image processing algorithms have a problem with handling the narrow field of view and poor quality of retinal images from a mobile phone. This paper enhances the accuracy of our previously proposed scheme, ADI-GVF snakes, to improve the segmentation of the optic disk (OD) and the optic cup (OC) for glaucoma pre-screening [1] from retinal images obtained from a mobile phone. This work integrated a better OD localization method, namely, the exclusion method (EM) with ADI-GVF segmentation for the OD and the OC. The improved algorithm can segment the regions of the OD and OC more accurately, resulting in a more precise value of the cup-to-disk area ratio (CDAR). The proposed method yields as high as 93.33% for true positive rate (TPR) and 93.87% for true negative rate (TNR) and as low as 6.12% and 6.66% for false omission rate (FOR), and false discovery rate (FDR). It also improves TPR, TNR, FOR, and FDR of the previous scheme [1] by 4.45%, 4.08%, 4.08%, and 4.44% respectively.

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“…As preprocessing authors applied 2D median filter and Multi threshold methods. Researches [38][39][40][41][42][43] state that the majority of classical CDR based approaches suffer inaccuracy in prediction, especially in OC detection over the horizontally identified disk region that makes overall detection output suspicious. In [45], authors pre-processed input with filtering, green channel extraction and CLAHE implementation to achieve better CDR estimation.…”

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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The comparison of GVF Snake Active Contour method and Ellipse Fit in optic disc detection for glaucoma diagnosis

Cited by 11 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+

Glaucoma Detection in Mobile Phone Retinal Images Based on ADI-GVF Segmentation with EM initialization

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

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