Automated detection of optic disc in retinal fundus images using gabor filter kernels

<p>Identifying the position of the optic disc on the retinal fundus image is a technique that is often used in medical diagnosis, treatment and monitoring processes. Determination of the intensity of the bright colors that belongs to the optic disc on a normal retinal image by the help of image processing algorithms is a fairly easy process. However, determining the optic disc on a retinal image including the diabetic retinopathy disease is a more difficult process. The reason for this difficulty is the existence of many regions that have the same light intensity in different parts of the retina. In this study, a new method for supplying the automatic determination of the optic disc in a recursive manner is proposed. By the help of OpenCV library, automatic determination process of the optic disc on the retinal fundus images including the diabetic retinopathy disease, has been implemented. Circular regions with maximum brightness values in the retinal images that were normalized and passed through the denoising process were determined and these regions were analyzed if they are optic disc or not. This process basically consists of two steps: In the first step, the possible optic disc candidate regions were determined recursively and in the second step, by the help of Gabor filter kernels, these regions were analyzed and it’s provided to decide if they are optic disc or not. This study is based on a dataset that has 89 images including diabetic retinopathy disease. Performance of this system is tested on these images and also on the images that the red, green, blue color channels and Contrast Limited Adaptive Histogram Equalization (CLAHE) retinas were obtained. Most accurate determination of the position of the optic disc is obtained with retinas, implemented process CLAHE, including the best success rate of 89.88%.</p><p> </p>Keywords: Optic disc, diabetic retinopathy, recursively, circular region, gabor filter kernels.

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Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening

IEEE Access ◽

10.1109/access.2018.2890544 ◽

2019 ◽

Vol 7 ◽

pp. 8527-8541 ◽

Cited By ~ 4

Author(s):

Jiapan Guo ◽

George Azzopardi ◽

Chenyu Shi ◽

Nomdo M. Jansonius ◽

Nicolai Petkov

Keyword(s):

Fundus Images ◽

Automatic Determination ◽

Glaucoma Screening ◽

Retinal Fundus Images ◽

Retinal Fundus ◽

Cup To Disc Ratio

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760.v1 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

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Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

Recent Patents on Engineering ◽

10.2174/1872212114666200109103922 ◽

2020 ◽

Vol 14 ◽

Author(s):

Charu Bhardwaj ◽

Shruti Jain ◽

Meenakshi Sood

Keyword(s):

Diabetic Retinopathy ◽

Statistical Analysis ◽

Early Stage ◽

Image Feature ◽

Grading System ◽

Fundus Images ◽

Fundus Image ◽

Retinal Fundus Images ◽

Retinal Fundus ◽

Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

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Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sensors ◽

10.3390/s21113922 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3922

Author(s):

Sheeba Lal ◽

Saeed Ur Rehman ◽

Jamal Hussain Shah ◽

Talha Meraj ◽

Hafiz Tayyab Rauf ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Feature Fusion ◽

Speckle Noise ◽

Fundus Images ◽

Adversarial Examples ◽

Adversarial Training ◽

Adversarial Attack ◽

Retinal Fundus Images ◽

Attacks And Defenses ◽

Retinal Fundus

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

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Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images

Applied Sciences ◽

10.3390/app10113833 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3833 ◽

Cited By ~ 3

Author(s):

Haidar Almubarak ◽

Yakoub Bazi ◽

Naif Alajlan

Keyword(s):

Optic Nerve ◽

Optic Disc ◽

Optic Nerve Head ◽

Fine Tuning ◽

Fundus Images ◽

Two Stage ◽

Second Stage ◽

Retinal Fundus Images ◽

Tuning Strategy ◽

Retinal Fundus

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.

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An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3469841 ◽

2021 ◽

Vol 17 (3s) ◽

pp. 1-24

Author(s):

Mohammad Shorfuzzaman ◽

M. Shamim Hossain ◽

Abdulmotaleb El Saddik

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Vision Loss ◽

Final Diagnosis ◽

Learning Rate ◽

Ensemble Model ◽

Fundus Images ◽

Robust Detection ◽

Retinal Fundus Images ◽

Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

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