Three Sigma and Multilevel Active Contour Approach to Detect Optic Disc and Optic Cup

Author(s):  
N C Patil ◽  
P V Rao
Keyword(s):  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Wei Zhou ◽  
Yugen Yi ◽  
Yuan Gao ◽  
Jiangyan Dai

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.


2016 ◽  
Vol 37 (3) ◽  
pp. 701-717 ◽  
Author(s):  
Ahmed Almazroa ◽  
Sami Alodhayb ◽  
Essameldin Osman ◽  
Eslam Ramadan ◽  
Mohammed Hummadi ◽  
...  
Keyword(s):  

Author(s):  
D. Shriranjani ◽  
Shiffani G. Tebby ◽  
Suresh Chandra Satapathy ◽  
Nilanjan Dey ◽  
V. Rajinikanth

2020 ◽  
Vol 34 (01) ◽  
pp. 751-758
Author(s):  
Ge Li ◽  
Changsheng Li ◽  
Chan Zeng ◽  
Peng Gao ◽  
Guotong Xie

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.


Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4401 ◽  
Author(s):  
Yong-li Xu ◽  
Shuai Lu ◽  
Han-xiong Li ◽  
Rui-rui Li

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.


Sign in / Sign up

Export Citation Format

Share Document