A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model

Abdullah, Ahmad S.; Rahebi, Javad; Özok, Yasa Ekşioğlu; Aljanabi, Mohanad

doi:10.1007/s11517-019-02032-8

Cited by 25 publications

(10 citation statements)

References 37 publications

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“…A lot of research has been done to propose segmentation algorithms of the GA. Standard algorithms have been applied, such as region growing [ 4 , 5 ] or active contour [ 6 ], region oriented variational methods with level set implementation [ 7 , 8 ], texture analysis [ 9 ] and watershed segmentation [ 10 , 11 ]. Most of the proposed methods are based on supervised machine learning approaches, with statistical models [ 12 ], random forests [ 13 ], random forests combined with Support Vector Machine [ 14 ] or k-nearest neighbor classifiers [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

et al. 2021

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Age-related macular degeneration (ARMD), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such is widely used in clinical trials yet only relies on manual segmentation. Artificial intelligence, in particular automatic image analysis based on neural networks, has a major role to play in better understanding the disease, by analyzing the intrinsic optical properties of dry ARMD lesions from patient images. In this paper, we propose a comparison of automatic segmentation methods (classical computer vision method, machine learning method and deep learning method) in an unsupervised context applied on cSLO IR images. Among the methods compared, we propose an adaptation of a fully convolutional network, called W-net, as an efficient method for the segmentation of ARMD lesions. Unlike supervised segmentation methods, our algorithm does not require annotated data which are very difficult to obtain in this application. Our method was tested on a dataset of 328 images and has shown to reach higher quality results than other compared unsupervised methods with a F1 score of 0.87, while having a more stable model, even though in some specific cases, texture/edges-based methods can produce relevant results.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

et al. 2021

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“…It was proposed in a previous research [31]. To localize and segment the optic disc in human retina images, a new approach was projected in a research paper [32] that used Fuzzy Clustering Algorithm (FCA).…”

Section: Literature Reviewmentioning

confidence: 99%

Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms

Alsarori¹,

Kaya²,

Rahebi³

et al. 2020

IJCIS

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Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability.

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“…Abdullah et al [12] the OD was segmented using an active contour model based on FCM clustering. Morphological operations were applied to enhance and normalize the image.…”

Section: Related Workmentioning

confidence: 99%

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

Aouf¹,

Ali²,

Kareem³

2020

International Journal of Engineering Research and Technology

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A retinal image is an essential tool for ophthalmology diagnosis. It is necessary for the early detection of many retinal diseases including glaucoma. Glaucoma acquires a second place in the leading causes of blindness worldwide. It damages the optic nerve which is responsible for connecting 1.2 million different nerve fibers to the brain. Once the optic nerve is destroyed it can not be treated but its early diagnosis and treatment can prevent the vision loss. The detection of glaucoma is usually performed by measuring the ratio between the optic disc (OD) and the optic cup (OC). The average ratio between the OD and the OC is 0.3. If it is higher than 0.3, then it is an indication of glaucoma. This paper aims to present an image processing algorithm for automatic identification of glaucoma by using active contour (snake) model to contour the OD and the OC boundaries that will help calculate the cup to disk ratio (CDR). The proposed model will help ophthalmologists to monitor any changes occur in the optic nerve.

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A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model

Cited by 25 publications

References 37 publications

Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

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