Document Type : Full Research Paper


Assistant Professor, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran



Intuitive examination of retinal layers in Spectral-Domain Optical Coherence Tomography (SD-OCT) images is one of the main methods used by physicians to diagnose retinal diseases. This method faces challenges such as noise and image complexity and the proximity of retinal layers. In recent years, the automatic diagnosis of retinal diseases has become an important clinical issue in computer vision. In this research, a new method for efficient multi-class automatic classification of SD-OCT images has been proposed. This method consists of five stages, preprocessing, layer recognition, feature extraction, and image classification. Examination of the shape of the RNFL layer and IS/OS junction as a clinical method is influential in physicians' decisions to diagnose retinal diseases. Therefore, in this study, inspired by this clinical diagnosis method, the RNFL layer, and the IS/OS junction have been detected by a new method based on the Frangi vessel enhancement algorithm and the gradient of the image. Then, by extracting and selecting several efficient features from the curves of the layers, an algorithm based on the ensemble decision tree has been proposed for classifying SD-OCT images of the retina and presented as a MATLAB application. The proposed method has been evaluated using images of two well-known databases of Duke and Kermany. Based on the results, precision, sensitivity, specificity, accuracy, miss rate and F1-score of the proposed method in Duke database were equal to 98.7, 98.8, 99.4, 99.1, 1.3, and 98.7, respectively, and in Kermany database were 96.8, 96.7, 98.9, 98.4, 3.2 and 96.7 respectively. The results show the superiority of the proposed method compared to other comparative methods. In summary, the use of efficient features of retinal effective layers and a powerful algorithm for classification has improved the performance of the proposed method compared to previous more complex methods.


Main Subjects

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