Segmentation of the optic disc and optic cup using a machine learning-based biregional contour evolution model for the cup-to-disc ratio

2022 ◽  
Author(s):  
Lingling Fang ◽  
Lirong Zhang
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Evolution Model ◽  
Optic Cup ◽  
Contour Evolution ◽  
Cup To Disc Ratio

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Joint Optic Disc and Dup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

2020 ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Post Process ◽  
Main Indicator ◽  
Segmentation Methods ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio is the main indicator used to screen and diagnose glaucoma, optic disc and optic cup segmentation can assist computer in diagnosing glaucoma.Therefore, accurate segmentation of optic disc and optic cup is beneficial to the screening and diagnosis of glaucoma and helps patients diagnose and treat early. Method: In this paper, we consider the segmentation of the optic disc and the optic cup as a multi-category semantic segmentation task, and proposed a deep learning-based model model named DDSC-Net(densely connected depthwise separable convolution network) to extract the optic disc and the optic cup. The backbone network is composed of densely connected deep separable convolution blocks to form a deeper network and the image pyramid input is introduce into the input layer to widen the network. In the post-process, we apply the morphological method to refine the output segmentation results. Result: The proposed method is evaluated on two publicly available datasets, DRISHTI-GS dataset and REFUGE dataset. And the experiment results show that our DDSC-Net outperforms state-of-the-art optic disc and cup segmentation methods in terms of disc coefficients and Jaccard score. Furthermore,our method achieves the best result on the more challenging optic cup segmentation task. Conclusion: The promising segmention performances reveal that our method has potential in assisting the screening and diagnosis of the glaucoma.

scholarly journals Improved OD and OC Fragmentation using Deep Learning for the Progression Recognition of Glaucoma

2021 ◽  
Author(s):  
Shanmugam P ◽  
Raja J ◽  
Pitchai R
Keyword(s):  
Deep Learning ◽  
General Population ◽  
Optic Disc ◽  
State Of The Art ◽  
Initial Stage ◽  
Optic Cup ◽  
Overall Performance ◽  
Cup To Disc Ratio

Abstract Glaucoma is one of the most hazardous diseases, proceeding to impact and burden an extensive bit of our general population. Appropriately, the initial stage of identification of glaucoma is significant to prevent the permanent vision misfortune. The CDR is the important factor for glaucoma recognition. The precise fragmentation of optic disc and cup is yet an evolving issue. Most of the segmentation based glaucoma recognition methods depends on the handcrafted features. It affects the overall performance of the glaucoma recognition. To resolve this issue, an efficient deep learning based optic cup and disc segmentation using technique multi-label segmentation Au-net has been developed in this paper. The proposed method focusing on the optic cup-to-disc ratio for the recognition of glaucoma, which may be the best system for building a capable, energetic, and accurate structure for glaucoma analysis. This system has been simulated on DRISHTI datasets. The exploratory outcomes indicates the proposed strategy performs well to the best with state-of-the-art methodologies accomplishing a 99% of Accuracy, 88% of Sensitivity and 95.5% of Specificity on the DRISHTI GS1 dataset individually.

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Klasifikasi Glaukoma Menggunakan Artificial Neural Network

2020 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Alva Rischa Qhisthana Pratika ◽  
Rita Magdalena ◽  
R Yunendah Nur Fuadah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optic Disc ◽  
Training Data ◽  
Morphological Operations ◽  
Morphological Operation ◽  
Optic Cup ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cup To Disc Ratio

Abstract Glaucoma is an eye disease caused by increased eyeball pressure resulting in damage to the optic nerve and the second leading cause of blindness after cataracts. Nerve damage often occurs without symptoms so that an early examination can reduce the risk of glaucoma. Therefore, the authors designed a glaucoma detection system through eye fundal images that can facilitate the detection of glaucomaby extracting various features like Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), and Horizontal to Vertical CDR (H-V CDR) with Morphological Operations dan Thresholding for segmentation of Optic Disc (OD) and Optic Cup (OC). Artificial Neural Network (ANN) is used as a classifier of glaucoma. Through this method, the test data can be divided into two classifications namely normal eyes and glaucoma eyes. 62 pieces of data will be trained and 62 pieces of data will be tested. The results obtained aim to facilitate early detection of glaucoma eyes. Accuracy on training data reaches 100% and accuracy in this study is reached 93.5484%.Keyword: Glaucoma, Morphological Operation, Thresholding, Artificial Neural Network AbstrakGlaukoma adalah penyakit mata yang disebabkan oleh peningkatan tekanan bola mata sehingga terjadi kerusakan saraf optik dan dapat menyebabkan kebutaan nomor dua setelah katarak. Kerusakan saraf sering terjadi tanpa gejala sehingga pemeriksaan dini dapat mengurangi resiko dari glaukoma. Oleh karena itu, penulis merancang suatu sistem untuk mendeteksi glaukoma melalui citra fundus mata dengan mengekstraksi beberapa fitur yaitu Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), dan Horizontal to Vertical CDR (H-V CDR) dengan mengsegmentasi Optic Disc (OD) dan Optic Cup (OC) dengan menggunakan metode Morphological Operations dan Thresholding. Artificial Neural Network (ANN) digunakan sebagai metode klasifikasi glaukoma. Melalui metode tersebut, data uji dapat dibagi dalam dua klasifikasi yaitu mata normal dan mata glaukoma. Data latih yang akan diambil sebanyak 62 buah dan data uji yang akan diambil sebanyak 62 buah. Hasil yang diperoleh bertujuan untuk memudahkan mendeteksi secara dini mata glaukoma. Akurasi pada data latih mencapai 100% dan akurasi pada data uji mencapai 93,5484%.Kata kunci: Glaukoma, Morphological Operation, Thresholding, Artificial Neural Network

scholarly journals Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

2020 ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Important Indicator ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider networkto improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 %on the REFUGE dataset, respectively.Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

scholarly journals Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-28 ◽  
Author(s):  
Ahmed Almazroa ◽  
Ritambhar Burman ◽  
Kaamran Raahemifar ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Performance Metrics ◽  
Future Research ◽  
Geometrical Parameters ◽  
Optic Cup ◽  
Future Research Directions ◽  
And Performance ◽  
Retinal Fundus ◽  
Cup To Disc Ratio

Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.

scholarly journals Joint Optic Disc and Dup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

2020 ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Post Process ◽  
Main Indicator ◽  
Segmentation Methods ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio is the main indicator used to screen and diagnose glaucoma, optic disc and optic cup segmentation can assist computer in diagnosing glaucoma.Therefore, accurate segmentation of optic disc and optic cup is beneficial to the screening and diagnosis of glaucoma and helps patients diagnose and treat early. Method: In this paper, we consider the segmentation of the optic disc and the optic cup as a multi-category semantic segmentation task, and proposed a deep learning-based model model named DDSC-Net(densely connected depthwise separable convolution network) to extract the optic disc and the optic cup. The backbone network is composed of densely connected deep separable convolution blocks to form a deeper network and the image pyramid input is introduce into the input layer to widen the network. In the post-process, we apply the morphological method to refine the output segmentation results. Result: The proposed method is evaluated on two publicly available datasets, DRISHTI-GS dataset and REFUGE dataset. And the experiment results show that our DDSC-Net outperforms state-of-the-art optic disc and cup segmentation methods in terms of disc coefficients and Jaccard score. Furthermore,our method achieves the best result on the more challenging optic cup segmentation task. Conclusion: The promising segmention performances reveal that our method has potential in assisting the screening and diagnosis of the glaucoma.

scholarly journals CUP and DISC OPTIC Segmentation Using Optimized Superpixel Classification for Glaucoma Screening

2014 ◽  
Vol 3 (03) ◽  
pp. 07-11
Author(s):  
Prof. Vaishali Sarangpure
Keyword(s):  
Neural Network ◽  
Optic Disc ◽  
Glaucoma Screening ◽  
Incurable Disease ◽  
Optic Cup ◽  
Cup To Disc Ratio

Glaucoma, an incurable disease related to eyes which results in loss of the vision. Identifying this disease within in a proper period of time is most important, since it cannot be cured. The important aspect of this paper is to detect glaucoma at initial stages. Segmentation in the optic disc necessitates the differentiation of each super pixel by employing Histograms, centre surround statistics. Information location in merged with the above methods in increasing the performance of optic cup segmentation. Optic disc and optic cups are employed to evaluate cup to disc ratio of the disease identified. Neural network is used to extract the patterns and also to detect glaucomatous cells that are too complex to be noticed by either humans or other computer techniques.

scholarly journals Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Important Indicator ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7$$\%$$ % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79$$\%$$ % on the REFUGE dataset, respectively. Conclusions The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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