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We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice(More)
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state(More)
PURPOSE To develop an automated method to identify the normal macula and three macular pathologies (macular hole [MH], macular edema [ME], and age-related macular degeneration [AMD]) from the fovea-centered cross sections in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images. METHODS A sample of SD-OCT macular scans(More)
We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice(More)
We propose a 2D continuous-time Hidden Markov Model (2D CT-HMM) for glaucoma progression modeling given longitudinal structural and functional measurements. CT-HMM is suitable for modeling longitudinal medical data consisting of visits at arbitrary times, and 2D state structure is more appropriate for glaucoma since the time courses of functional and(More)
In this paper, we propose two efficient approaches for Named Entity recognition (NER) from spoken documents. The first approach used a very efficient data structure, the PAT trees, to extract global evidences from the whole spoken documents , to be used with the well-known local (internal and external) evidences popularly used by conventional approaches.(More)
We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the(More)
1 Purpose: To develop an automated method to identify the normal macula and three macular 2 pathologies (macular hole (MH), macular edema (ME), and age-related macular degeneration 3 (AMD)) from the fovea-centered cross sections in three-dimensional (3D) spectral domain optical 4 coherence tomography (SD-OCT) images.
For improving the working Life of engine cyLinder Liner, using relevant equipments, instruments and methods, the test studys the wear-resistant treatment to CyLinder Liner Inner Surface. The results showed that: after plasma beam scanning, cyLinder Liner inner surface achieved self-quenching depend on self fast cooLing and obtained aphanic martensitic(More)
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