OIPAV (Ophthalmic Images Processing, Analysis and Visualization) is a cross-platform software which is specially oriented to ophthalmic images. It provides a wide range of functionalities including data I/O, image processing, interaction, ophthalmic diseases detection, data analysis and visualization to help researchers and clinicians deal with various ophthalmic images such as optical coherence tomography (OCT) images and color photo of fundus, etc. It enables users to easily access to different ophthalmic image data manufactured from different imaging devices, facilitate workflows of processing ophthalmic images and improve quantitative evaluations. In this paper, we will present the system design and functional modules of the platform and demonstrate various applications. With a satisfying function scalability and expandability, we believe that the software can be widely applied in ophthalmology field.
In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segment
retinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includes
two parts: preprocessing and layer segmentation. During the preprocessing phase, images is first filtered for denoising,
then the B-scans are flattened. During layer segmentation, the AAM is first used to obtain the coarse segmentation
results. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficiently
integrated into the graph search segmentation process. The proposed method was tested on a dataset which
contained113-D SD-OCT images, and compared to the manual tracings of two observers on all the volumetric scans. The
overall mean border positioning error for layer segmentation was found to be 7.09 ± 6.18μm for normal subjects. It was
comparable to the results of traditional graph search method (8.03±10.47μm) and mean inter-observer variability
(6.35±6.93μm).The preliminary results demonstrated the feasibility and efficiency of the proposed method.
In this paper, we sought to find a method to detect the Inner Segment /Outer Segment (IS/OS)disruption region automatically. A novel support vector machine (SVM) based method was proposed for IS/OS disruption detection. The method includes two parts: training and testing. During the training phase, 7 features from the region around the fovea are calculated. Support vector machine (SVM) is utilized as the classification method. In the testing phase, the training model derived is utilized to classify the disruption and non-disruption region of the IS/OS, and calculate the accuracy separately. The proposed method was tested on 9 patients' SD-OCT images using leave-one-out strategy. The preliminary results demonstrated the feasibility and efficiency of the proposed method.