Abstract: Automated and objective methods for quantifying a retinal characteristic include segmenting an optical coherence tomography retinal image into a plurality of layered retinal regions, and quantifying the retinal characteristic for each region as normalized to a range defined by the characteristic value in the vitreous region and in the retinal pigment epithelium region. Such methods are useful for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies.

Abstract: Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

Abstract: Automated and objective methods for quantifying a retinal characteristic include segmenting an optical coherence tomography retinal image into a plurality of layered retinal regions, and quantifying the retinal characteristic for each region as normalized to a range defined by the characteristic value in the vitreous region and in the retinal pigment epithelium region. Such methods are useful for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies.

Abstract: Automated and objective methods for quantifying a retinal characteristic include segmenting an optical coherence tomography image into a plurality of layered retinal regions, and quantifying the retinal characteristic for each region as normalized to a range defined by the characteristic value in the vitreous region and in the retinal pigment epithelium region. Such methods are useful for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies.

Abstract: Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

Abstract: Embodiments of the present invention relate to a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effect across a high number of spinal cord epidural stimulation parameters. This new method is designed to automate the current process for performing this task that has been accomplished manually by data analysts through observation of the raw EMG signals, which is laborious and time-consuming as well as being prone to human errors. The proposed method provides fast and accurate framework for activation detection and visualization of the results within five main algorithms.

Abstract: Automated and objective methods for quantifying a retinal characteristic from a retinal Optometric Coherence Tomography retinal image, and methods for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies based on the quantification method are disclosed.

Abstract: A method for detecting a nodule in image data including the steps of segmenting scanning information from an image slice to isolate lung tissue from other structures, resulting in segmented image data; extracting anatomic structures, including any potential nodules, from the segmented image data, resulting in extracted image data; and detecting possible nodules from the extracted image data, based on deformable prototypes of candidates generated by a level set method in combination with a marginal gray level distribution method.

Abstract: A method for detecting a nodule in image data including the steps of segmenting scanning information from an image slice to isolate lung tissue from other structures, resulting in segmented image data; extracting anatomic structures, including any potential nodules, from the segmented image data, resulting in extracted image data; and detecting possible nodules from the extracted image data, based on deformable prototypes of candidates generated by a level set method in combination with a marginal gray level distribution method.