Ant Colony Optimization-based method for optic cup segmentation in retinal images

Arnay, Rafael; Fumero, Francisco; Sigut, J.

doi:10.1016/j.asoc.2016.10.026

Cited by 53 publications

(22 citation statements)

References 18 publications

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“…Many different approaches to segmenting of the OD and/or OC in fundus images have been proposed in the literature. The existing methods for automated OD and OC segmentation in fundus images can be broadly classified into three main categories: shape-based template matching [3][4][5][6][7][8][9], active contours and deformable based models [10][11][12][13][14][15][16][17][18], and more recently, machine and deep learning methods [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. We give a brief overview of the existing methods below.…”

Section: Introductionmentioning

confidence: 99%

“…(b) Active contours and deformable based models: These methods have been widely applied for the segmentation of the OD and OC [10][11][12][13][14][15][16][17][18]. Active contours approaches are deformable models which convert the segmentation problem into an energy minimisation problem where different energies are derived to reflect features in the image such as intensity, texture and boundary smoothness.…”

Section: Introductionmentioning

confidence: 99%

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Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

et al. 2018

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Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR); however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC). The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method's performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

et al. 2018

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“…A superpixel (SP) for glaucoma diagnosis is established discovering framework on retinal construction. This method utilizes constituent image automatically and localizes the optic cup for recognizing glaucoma in digital fundus photographs (Arnay et al ; Xu et al, ). The main contributions provided by this method are three.…”

Section: Pattern Classification and Machine Learning Methods For Glaumentioning

confidence: 99%

Fundus image classification methods for the detection of glaucoma: A review

Saba

Bokhari

Sharif

et al. 2018

Microscopy Res & Technique

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Glaucoma is a neurodegenerative illness and is considered as a standout amongst the most widely recognized reasons for visual impairment. Nerve's degeneration is an irretrievable procedure, so the diagnosis of the illness at an early stage is an absolute requirement to stay away from lasting loss of vision. Glaucoma effected mainly because of increased intraocular pressure, if it is not distinguished and looked early, it can result in visual impairment. There are not generally evident side effects of glaucoma; thus, patients attempt to get treatment just when the seriousness of malady is advanced altogether. Determination of glaucoma often comprises of review of the basic crumbling of the nerve in conjunction with the examination of visual function capacity. This article shows the persistent illustration of glaucoma, its side effects, and the potential people inclined to this malady. The essence of this article is on different classification methods being utilized and proposed by various scientists for the identification of glaucoma. This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma. The research related to the findings and the treatment is likewise evaluated in this article.

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“…Nevertheless, their proposed application was less impressive in estimating the cup-to-disc (CDR). In a previous study [39], an ant colony optimization (ACO) algorithm was considered for OC segmentation. Their software reported an AUC of 0.79.…”

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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Ant Colony Optimization-based method for optic cup segmentation in retinal images

Cited by 53 publications

References 18 publications

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

Fundus image classification methods for the detection of glaucoma: A review

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

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