Automatic optical coherence tomography imaging analysis for retinal disease screening
AuthorHussain, Md Akter
AffiliationComputing and Information Systems
Document TypePhD thesis
Access StatusOpen Access
© 2017 Dr. Md Akter Hussain
The retina and the choroid are two important structures of the eye and on which the quality of eye sight depends. They have many tissue layers which are very important for monitoring the health and the progression of the eye disease from an early stage. These layers can be visualised using Optical Coherence Tomography (OCT) imaging. The abnormalities in these layers are indications of several eye diseases that can lead to blindness, such as Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD) and Glaucoma. If the retina and the choroid are damaged there is little chance to recover normal sight. Moreover, any damage in them will lead to blindness if no or late treatment is administered. With eye diseases, early detection and treatment are more effective and cheaper. Biomarkers extracted from these tissue layers, such as changes in thickness of the layers, will note the presence of abnormalities called pathologies such as drusen and hyper-reflective intra-retinal spots, and are very effective in the early detection and monitoring the progression of eye disease. Large scale and reliable biomarker extraction by manual grading for early detection is infeasible and prone to error due to subjective bias and are also cost ineffective. Automatic biomarker extraction is the best solution. However, OCT image analysis for extracting biomarkers is very challenging because of noisy images, low contrast, extremely thin retinal layers, the presence of pathologies and complex anatomical structures such as the optic disc and macula. In this thesis, a robust, efficient and accurate automated 3D segmentation algorithm for OCT images is proposed for the retinal tissue layers and the choroid, thus overcoming those challenges. By mapping OCT image segmentation problem as a graph problem, we converted the detection of layer boundaries to the problem of finding the shortest paths in the mapped graph. The proposed method exploits layer-oriented small regions of interest, edge pixels from canny edge detections as nodes of the graph, and incorporates prior knowledge of the structures into edge weight computation for finding the shortest path using Dijkstra’s shortest path algorithm as a boundary of the layers. Using this segmentation scheme, we were able to segment all the retinal and choroid tissue layers very accurately and extract eight novel biomarkers such as attenuation of the retinal nerve fibre layer, relative intensity of the ellipsoid zone, thickness of the retinal layers, and volume of pathologies i.e. drusen, etc. In addition, we demonstrated that using these biomarkers provides a very accurate (98%) classification model for classifying eye patients into those with normal, DME and AMD diseases which can be built using a Random Forest classifier. The proposed segmentation method and classification method have been evaluated on several datasets collected locally at the Center for Eye Research Australia and from the public domain. In total, the dataset contains 56 patients for the evaluation of the segmentation algorithms and 72 patients for the classification model. The method developed from this study has shown high accuracy for all layers of the retina and the choroid over eight state-of-the-art methods. The root means square error between manually delineated and automatically segmented boundaries is as low as 0.01 pixels. The quantification of biomarkers has also shown a low margin of error from the manually quantified values. Furthermore, the classification model has shown more than 98% accuracy, which outperformed four state-of-the-art methods with an area under the receiver operating characteristic curve (AUC) of 0.99. The classification model can also be used in the early detection of diseases which allows significant prevention of blindness as well as providing a score/index for the condition or prediction of the eye diseases. In this thesis, we have also developed a fully automated prototype system, OCTInspector, for OCT image analysis using these proposed algorithms and methods.
Keywordsimage processing; graph theory; machine learning; optical coherence tomography; retinal disease screening
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