Optic Disk Segmentation Using Histogram Analysis

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR databas

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.</jats:p

Modern Methods of Detection and Features Extraction of Optical Disc from Retinal Images

Diplomová práce se zabývá testováním metod automatické segmentace pro detekci optického disku. Pro analýzu se využívaly datasety Clarity RetCam 3, Envision RetCam 3, Phoenix ICON, Data Drive a Data Stare. Celkově bylo zpracováno 232 snímků sítnice dospělých pacientů a dětí s onemocněním Retinopatie nedonošených (ROP). Počáteční částí bylo manuální zpracování obrázků, kde výsledkem byl zlatý standard. Klíčovou částí byl návrh algoritmů pro zpracování obrazových dat. Nejprve byl obraz předzpracován s využitím metody CLAHE a Bilaterálního filtru. Aktivní kontury bez hran tvořily algoritmus segmentace. Metoda využívala volně se deformovatelných křivek. Výsledkem byl binární obraz, který se porovnával se zlatým standardem. Rozdílnosti mezi metodami byly vyhodnocovány pomocí evaluačních parametrů. Došlo tak ke zhodnocení efektivity metod segmentace pro různé zobrazovací modality. Na závěr bylo vytvořeno grafické uživatelské rozhraní pomocí programu MATLAB.The master thesis focused on testing the methods of automatic segmentation for detection of optical disc. Datasets Clarity RetCam 3, Envision RetCam 3, Phoenix ICON, Data Drive and Data Stare was used for the analysis. Altogether, 232 images of retina of adult patients and children diagnosed with retinopathy of prematurity (ROP) were analyzed. The first part was a manual processing of the images, where the golden standard came as a result. The core part was a proposal of algorithms for processing the data images. Firstly, an image was preprocessed using the method CLAHE and Bilateral filter. Active contours without the edges were used to make the segmentation algorithm. The method used freely deformed curves. The result received from this action was a binar imagge, wich was then compared to the golden standard. The differences between the methods were evaluated based on the evauation parameters. Therefore, the effecivity of methods of segmentation for different displaying modalities were evaluated. Finally, the graphical user interface (GUI) was made in the MATLAB program.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis