Segmentation of Optic Disc in Fundus Images Using an Active Contour

2018 ◽  
Vol 16 (1) ◽  
pp. 97-111 ◽  
Author(s):  
A. Elbalaoui ◽  
Y. Ouadid ◽  
A. Merbouha
Keyword(s):  
Blood Vessels ◽  
Optic Disc ◽  
Retinal Image ◽  
Active Contour ◽  
Active Contour Model ◽  
Ground Truth ◽  
Automated Detection ◽  
Major Purpose ◽  
Ground Truth Image ◽  
Novel Method

A successful optic disc (OD) segmentation is an important task for automated detection white lesions related to diabetic retinopathy. Therefore, exudate detection is the authors' major purpose, but they must extract the OD prior to the process because it appears with similar color, intensity and contrast to other characteristics of the retinal image. The retinal image consists of blood vessels that emerge from the OD. The presence of these blood vessels may act as a disturbance for the detection of OD. This article presents a novel method for segmentation of the OD in retinal images. The methodology includes localization of the OD center, followed by elimination of vascular structure using an inpainting method. Finally, an active contour model was applied to boundary OD segmentation. The results are compared with a ground truth image from the ophthalmologist. The source retinal image for performing this work was obtained from the publicly available DRIVE and MESSIDOR databases. This method offers a successful segmentation of OD which may help in diagnosis of various retinal abnormalities.

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

Empirical Evaluation of Retinal Vessel Segmentation Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4671-4677
Author(s):  
M. Vijaya Maheswari ◽  
G. Murugeswari
Keyword(s):  
Blood Vessels ◽  
Active Contour ◽  
Active Contour Model ◽  
Empirical Evaluation ◽  
Region Growing ◽  
Retinal Vessel ◽  
Diabetic Patients ◽  
Neural Signals ◽  
Retinal Vessel Segmentation ◽  
The Brain

Human eye is made up of millions of blood vessels. Retina is a delicate layer that covers the back portion of the eye. The role of retina is to transmit the light signals into neural signals to the brain which is then interpreted. The interpretation from the brain is converted as visual perceptions. Blood vessels supply a large amount of blood to the retina. When the level of glucose is high in the blood, the blood vessels in the retina gets damaged. In the advanced stages of damaged blood vessels, it leads to blindness. Various retinal diseases are caused by the damage in the vessels. One of the most threatening disease in the recent days is Diabetic Retinopathy (DR) in diabetic patients. In this paper, few segmentation techniques like Active Contour model, Thresholding based method and Region Growing methods are implemented. The performance of these techniques are analyzed in measures of Accuracy, Sensitivity, and Specificity. DRIVE and CHASE_DB1 dataset is used for this purpose. The outcome of this comparative analysis on DRIVE dataset shows that thresholding technique produces an accuracy of 95.06% and sensitivity of 88.30%, region growing technique produces a specificity of 97.24%, on CHASE_DB1 dataset results show that thresholding technique produces an accuracy of 94.74%, region growing produces an sensitivity of 77.08% and active contour produces a specificity of 97.06% respectively.

scholarly journals Design and Development of Street Crack Detection

2018 ◽  
Vol 7 (3.8) ◽  
pp. 82 ◽  
Author(s):  
Mr Swapnil Vilas Patil ◽  
Prof. Mangesh M. Ghonge ◽  
. .
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Ground Truth ◽  
Automated Detection ◽  
Driving Safety ◽  
Detection Accuracy ◽  
False Detection ◽  
Safety Assurance ◽  
Ground Truth Image ◽  
Better Than

Automated detection of street cracks is a crucial project. In transportation preservation for driving safety assurance and detection a crack manually is an exceptionally tangled and time excessive method. So with the advance of science and generation, automated structures with intelligence have been accustomed examine cracks instead of people. For crack detection and characterization image processing is used widely. But because of the inhomogeneity along the cracks, the inference of noise with the same texture and complexity of cracks, image processing remain challenging. In this paper, we focused on the system performance and the additional features. System which has crack detection accuracy issue, false detection of crack issue, efficiency issue are solved in this system. For better accuracy in detecting crack and increasing the performance of the system we used the random forest algorithm. This system help to detect and characterized the crack and it find out crack from noise also i.e. it neglect the noise better than existing system. Similarly, proposed method find out the length of the crack width and depth of the crack from image with the help of ground truth image.   

scholarly journals GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1219-1227
Author(s):  
B. Jafrasteh ◽  
I. Manighetti ◽  
J. Zerubia
Keyword(s):  
Ground Truth ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Networks ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Optical Images ◽  
Novel Approach ◽  
Ground Truth Image ◽  
Novel Method

Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks to the ground truth images, the method is called Conditional Generative Adversarial Network (CGAN). We propose a new loss function for both the Generator and the Discriminator networks, to improve their accuracy. Using two criteria and a manually annotated optical image, we compare the generalization performance of the proposed method to that of a classical DCN architecture, U-net. The comparison demonstrates the suitability of the proposed CGAN architecture. Further work is however needed to improve its efficiency.

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.

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