Glaucoma diagnosis in fundus eye images using diversity indexes

Araujo, Jonas B.; Souza, Johnatan Carvalho; Neto, Otilio Paulo Silva; Sousa, Jefferson Alves de; Almeida, João Dallyson Sousa de; Paiva, Anselmo Cardoso de; Silva, Aristófanes Corrêa; Bráz, Geraldo; Gattass, Marcelo

doi:10.1007/s11042-018-6429-z

Cited by 16 publications

(8 citation statements)

References 31 publications

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“…Araujo [14] proposed a glaucoma diagnosis using diversity indexes for feature extraction. Features were selected using genetic algorithm and fed to SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Kirar¹,

Agrawal²

2019

IJIES

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Glaucoma is one of the foremost causes of blindness over the world. It develops slowly and damages optic nerve head. Existing methods of glaucoma detection are expensive and sluggish. Hence quick and low-priced methods are required. In this paper, a novel fully variational and adaptive computer based glaucoma detection using compact variational mode decomposition (CVMD) from fundus images is proposed. Efficient sub band images having narrow fourier bandwidth, clear and sharp boundaries are obtained using CVMD is the key idea of the proposed method. This gives robust estimate of features. Texture feature capture subtle variation and give more information therefore these features are extracted from efficient sub band images and helps to increase the glaucoma detection accuracy. Extracted features are normalized and classified using support vector machine classifier. The obtained classification accuracies are 86.67 %, 86.67 %, 85.42% and 89.18 % for three, five, eight and tenfold cross validation respectively with kernel parameter 2. The obtained accuracy, sensitivity, specificity, precision and Fmeasure are 89.18 %, 90 %, 85 %, 93.34 % and 89.34 % respectively for tenfold cross validation. Proposed method is found to be better compared to the existing.

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“…Araujo [14] proposed a glaucoma diagnosis using diversity indexes for feature extraction. Features were selected using genetic algorithm and fed to SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Kirar¹,

Agrawal²

2019

IJIES

View full text Add to dashboard Cite

show abstract

“…Maheshwari [10][11] proposed automated glaucoma detection approach in his two different works. His first work [10] Araujo [12] diversity indexes in their work for glaucoma detection. They used Genetic algorithm and SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Detection using Bi-Dimensional Empirical Mode Decomposition from Retinal Fundus Images

2021

IJIES

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“…Nevertheless, how to select the suitable image features and classifiers is the challenging issue. Apart from the aforementioned feature extraction strategy, the usage of the clinical indicators is another strategy to assess the optic disc, such as the vertical cup to disc ratio (CDR) [5], ISNT rule [6], and notching. Although these clinical indicators are different from each other, precise optic disc and optic cup boundary information is necessary.…”

Section: Introductionmentioning

confidence: 99%

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Zhou

Yuan

et al. 2019

Computational and Mathematical Methods in Medicine

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Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

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Glaucoma diagnosis in fundus eye images using diversity indexes

Cited by 16 publications

References 31 publications

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Glaucoma Detection using Bi-Dimensional Empirical Mode Decomposition from Retinal Fundus Images

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

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