Ophthalmology (Eye & Ear Hospital) - Research Publications

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Start date: 2010 × End date: 2019 × Subject: Artificial Intelligence and Image Processing ×

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    A Computer-Aided Diagnosis System of Nuclear Cataract
    Li, H ; Lim, JH ; Liu, J ; Mitchell, P ; Tan, AG ; Wang, JJ ; Wong, TY (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010-07)
    Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.
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    Automated Layer Segmentation of Optical Coherence Tomography Images
    Lu, S ; Cheung, CY-L ; Liu, J ; Lim, JH ; Leung, CK-S ; Wong, TY (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010-10)
    Under the framework of computer-aided diagnosis, optical coherence tomography (OCT) has become an established ocular imaging technique that can be used in glaucoma diagnosis by measuring the retinal nerve fiber layer thickness. This letter presents an automated retinal layer segmentation technique for OCT images. In the proposed technique, an OCT image is first cut into multiple vessel and nonvessel sections by the retinal blood vessels that are detected through an iterative polynomial smoothing procedure. The nonvessel sections are then filtered by a bilateral filter and a median filter that suppress the local image noise but keep the global image variation across the retinal layer boundary. Finally, the layer boundaries of the filtered nonvessel sections are detected, which are further classified to different retinal layers to determine the complete retinal layer boundaries. Experiments over OCT for four subjects show that the proposed technique segments an OCT image into five layers accurately.