Detection of the optic disc in fundus images by combining probability models

Harangi, Balázs; Hajdú, András

doi:10.1016/j.compbiomed.2015.07.002

Cited by 30 publications

(10 citation statements)

References 29 publications

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“…Various other techniques such as sliding window technique [ 91 ], Multi Resolution Gabor Transform [ 115 ], Gaussian kernels [ 144 ], intensity-based techniques [ 66 , 79 ], statistical classifier [ 5 , 61 ], Principal Component Analysis (PCA) [ 46 , 67 ], Singular Value Decomposition (SVD), Linear Discriminant Analysis (LDA), Semantic Image Transformation (SIT) [ 24 ], entropy-based backtracking approach [ 63 ], ON detection algorithm [ 133 ], deformable models [ 88 , 100 ] and Locally Statistical Active Contour Model with the Structure Prior (LSACM-SP) approach [ 146 ] are also used to accomplish the purpose of feature segmentation and extraction, for DR detection. DR classification is performed using Clustering [ 5 , 46 , 51 , 88 , 91 , 116 , 145 ], ensemble techniques [ 13 , 14 , 18 , 141 ], SVM [ 22 , 63 , 108 ], Sparse Representation Classifier (SRC) [ 71 ], Neural Networks [ 42 , 68 , 85 , 126 , 135 ], Random Forest Classifier (RFC) [ 61 , 62 , 125 ], SVM based hybrid classifier [ 5 ], Majority Voting (MV ) [ 53 ] etc. Supervised classification techniques such as KNN classification [ 37 , 93 ], Extreme Learning Machine (ELM) and Naive Bayes (NB) [ 17 ], Bayesian classifier [ 55 ], cascade Adaboost CNN classifier [ 8 ], Naïve–Bayes and Decision Tree (DT) C4.5 enhanced with bagging techniques [ 46 ], etc.…”

Section: Diagnosis Of Dr Using MLmentioning

confidence: 99%

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

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Section: Diagnosis Of Dr Using MLmentioning

confidence: 99%

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

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“…However, this model is highly dependent on preprocessing steps and algorithm parameters. Some other CAD algorithms have been developed based on the geometrical principle for OD segmentation with higher accuracy, such as the Hough circle cloud [28], ensemble of probability models [29], sliding band filter [30], and active contour models [31,32]. In a previous study [28], a fully automated software called Hough circle cloud was developed for OD localization.…”

Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

“…However, the software was tested on a small dataset and was implemented on powerful graphical processing units (GPUs) instead of local computers. The system proposed in [29] addressed the strengths of different object detection methods for OD segmentation. The authors achieved an accuracy of 98.91%.…”

Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…Regarding the deformable model-based approaches, they exploit the specific characteristics of the OD. Harangi et al [19] proposed a model based on the combination of probability models in order to detect the OD and its boundaries. Furthermore, they increased the accuracy of the method using axiomatic and Bayesian approximations.…”

Section: Optic Disk Detection Literaturementioning

confidence: 99%

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

et al. 2019

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Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively.

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Detection of the optic disc in fundus images by combining probability models

Cited by 30 publications

References 29 publications

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

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