An empirical study on optic disc segmentation using an active contour model

Mary, M. Caroline Viola Stella; Rajsingh, Elijah Blessing; Jacob, J. Kishore Kumar; Anandhi, D.; Amato, Umberto; Selvan, S.

doi:10.1016/j.bspc.2014.11.003

Cited by 63 publications

(32 citation statements)

References 32 publications

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“…(1 − H ( (y))) dy dx (12) A distance regularization term defined in [34] is added to Eq. (12) to have stable evolution of the level set function .…”

Section: Contour Detection Of Odmentioning

confidence: 99%

“…To date, several methods have been developed for the segmentation of the OD and cup regions from 2D color fundus images, with more methods on OD [11][12][13][14][15][16][17][18][19][20][21][22][23], but with fewer on cup [24][25][26][27][28] due to the cup's interweavement with blood vessels and surrounding tissues. A multiresolution sliding band filter (SBF) is applied for OD segmentation [11].…”

Section: Introductionmentioning

confidence: 99%

“…The OD center is found by applying the Circular Hough Transformation, and subsequently the disc boundary detection is done through active shape model (ASM) by defining 72 points around the disc. The gradient vector flow (GVF) is used to detect the boundary of OD [12,21]. A two-step methodology is used in [22]; first, the color morphology in Lab space is used to have homogeneous OD region, then the boundary of the OD is estimated by using level sets with variational formulation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma

Mittapalli¹,

Kande²

2016

Biomedical Signal Processing and Control

121

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“…(1 − H ( (y))) dy dx (12) A distance regularization term defined in [34] is added to Eq. (12) to have stable evolution of the level set function .…”

Section: Contour Detection Of Odmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma

Mittapalli¹,

Kande²

2016

Biomedical Signal Processing and Control

121

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“…We applied our approach to the datasets above and compared it with existing approaches including ASM [29][30][31], ACM [19,49] and Chan-Vese (C-V) [22,50] models. We used the mean Dice Coefficient D to measure the accuracy of the optic disc segmentation across different datasets and methods as shown in Table 5.…”

Section: Optic Disc Segmentation Accuracy Comparisonmentioning

confidence: 99%

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

et al. 2017

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“…For this purpose, various types of clustering and region prediction algorithms such as, level set method, 6 Active Contour Model (ACM), [7][8][9] fuzzy clustering 10 and k-means clustering 11,12 algorithms are proposed in the traditional works. Among these methods, then level set model performs the intensity and texturebased image segmentation processes by identifying the retinal optic disc in the image.…”

Section: Problem Identi¯cationmentioning

confidence: 99%

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Balakrishnan

2017

J. Innov. Opt. Health Sci.

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Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include ine±cient, inaccurate and estimates only the a®ected area. In order to solve these issues, a Neighboring Di®erential Clustering (NDC) -Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At¯rst, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identi¯cation. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, speci¯city, accuracy, Hausdor® distance, Jaccard and dice metrics.

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An empirical study on optic disc segmentation using an active contour model

Cited by 63 publications

References 32 publications

Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma

Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

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