Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma

Thakur, Niharika; Juneja, Mamta

doi:10.1016/j.bspc.2018.01.014

Cited by 114 publications

(62 citation statements)

References 45 publications

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“…In this section we briefly analyze the state-of-the-art techniques for glaucoma classification and OD/OC segmentation and their main evaluation issues. The interested reader could refer to the surveys by Almazroa et al (2015), Haleem et al (2013) and Thakur and Juneja (2018) for a comprehensive analysis of the previous non-deep learning based approaches. Chen et al (2015b) and Raghavendra et al (2018) proposed to use shallow architectures with a limited number of layers.…”

Section: Automated Glaucoma Assessment: State-of-the-art and Current mentioning

confidence: 99%

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Orlando

Breda

et al. 2020

Medical Image Analysis

498

248

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Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations.Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders.However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted * Corresponding authors: Yanwu Xu (ywxu@ieee.org) and Xiulan Zhang (

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Section: Automated Glaucoma Assessment: State-of-the-art and Current mentioning

confidence: 99%

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Orlando

Breda

et al. 2020

Medical Image Analysis

498

248

View full text Add to dashboard Cite

show abstract

“…Almotiri et al [5] provided an overview of algorithms to segment retinal vessels. Almazroa et al [6] and Thakur and Juneja [7] reviewed several methods for optic disc segmentation and diagnosis of glaucoma. However, expert knowledge is a prerequisite for hand-engineered features, and choosing the appropriate features requires intensive investigation of various options and tedious parameter settings.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

Asiri

Hussain

Adel

et al. 2019

Artificial Intelligence in Medicine

220

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Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deeplearning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.

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“…To find the optic cup, this study proposed an algorithm consists of preprocess and segmentation. In pre-process the blood vessel was removed to make the segmentation process of the optic cup easier, since blood vessel could cover the optic cup therefore affect the accuracy of the detection [9]. The segmentation process used an adaptive thresholding followed by morphological image processing such as convex hull, opening and erosion.…”

Section: Introductionmentioning

confidence: 99%

Optic cup segmentation using adaptive threshold and morphological image processing

Nugroho

Kirana

Pranowo

et al. 2019

CST

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Glaucoma is a chronic optic neuropathy. It was predicted that people with bilateral blindness caused by glaucoma will increase each year. Hence, computer-aided diagnosis of glaucoma was proposed to assist ophthalmologist to conduct a fast and accurate glaucoma screening. One of the ocular examination in screening is optic nerve examination called disc damage likelihood scale (DDLS). It is important to find the optic disc and the optic cup to determine the narrowest width of the neuroretinal rim when using DDLS. To find the optic cup, this study proposed a segmentation scheme consisting of pre-process, segmentation, convex hull and morphological opening operation. In pre-process the blood vessel was removed to make the segmentation process of the optic cup easier. The segmentation process was done by using an adaptive thresholding followed by morphological image processing such as convex hull, opening and erosion. This algorithm was applied on Magrabia dataset and attained accuracy, specificity and sensitivity of 99.50%, 99.75% and 75.19% respectively.

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Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma

Cited by 114 publications

References 45 publications

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

Optic cup segmentation using adaptive threshold and morphological image processing

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