Segmentation of Optic Disc by Localized Active Contour Model in Retinal Fundus Image

2018 ◽  
pp. 35-44 ◽  
Author(s):  
Shreenidhi H. Bhat ◽  
Preetham Kumar
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Fundus Image ◽  
Contour Model ◽  
Retinal Fundus Image ◽  
Retinal Fundus

Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images

2016 ◽  
Vol 36 (6) ◽  
pp. 795-809 ◽  
Author(s):  
Maitreya Maity ◽  
Dev Kumar Das ◽  
Dhiraj Manohar Dhane ◽  
Chandan Chakraborty ◽  
Anirudhha Maiti
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Fundus Images ◽  
Contour Model ◽  
Color Fundus Images

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

2015 ◽  
Vol 18 ◽  
pp. 19-29 ◽  
Author(s):  
M. Caroline Viola Stella Mary ◽  
Elijah Blessing Rajsingh ◽  
J. Kishore Kumar Jacob ◽  
D. Anandhi ◽  
Umberto Amato ◽  
...  
Keyword(s):  
Empirical Study ◽  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Model

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

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Wei Zhou ◽  
Yugen Yi ◽  
Yuan Gao ◽  
Jiangyan Dai
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Glaucoma Diagnosis ◽  
Contour Model ◽  
Novel Structure ◽  
Optic Cup ◽  
The One

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.

scholarly journals Early Detection Of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Level Set Segmentation

2019 ◽  
Vol 8 (5C) ◽  
pp. 1342-1349
Keyword(s):  
Early Detection ◽  
Optic Disc ◽  
Region Of Interest ◽  
Clinical Parameter ◽  
Fundus Image ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Standard Value ◽  
Retinal Fundus

Glaucoma is a malady of the optic nerve brought about by the expansion in the intraocular weight of the eye. It for the most part influences the optic plate by expanding the cup size. In this proposed method the clinical parameter such as vertical optic cup to disk ratio (CDR) is determined to identify the glaucomatous disease. The segmentation of optic disc (OD) and optic cup in retinal fundus image is an important step in the determination of CDR. Optic Disc is extracted from the fundus image by circular region of interest with Hough transformation. Linear regression fit is used to find the Gold standard value for the experimentally obtained CDR A Bayesian classifier is used to train the classifier set of CDR values obtained. Results produced from the classification obtain a accuracy of 94.28%, sensitivity of 94.38% and specificity of 94.11%. ROC curve is plotted to study the relation between specificity and sensitivity of the CDR and GSV. This proposed approach is robust in segmentation and the region boundaries are precise and is able to yield regions more homogeneous which can be used for objective mass screening of retinal images for early detection of Glaucoma.

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