University of Louisville Research Foundation, Inc.

University of Massachusetts

Mohammed Elmogy

Ayman Sabry El-Baz

Robert S Keynton

Ahmed Soliman

Shlomit Schaal

Harpal Sandhu

Gregory N Barnes

Nabila Eldawi

Fatmaelzahraa El-Gamal

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.

Segmentation of retinal blood vessels in optical coherence tomography angiography images

A non-invasive computer-aided diagnosis system generates a diagnosis of mild cognitive impairment, a disease state which often leads to the development of Alzheimer's disease. The system uses as inputs both functional positron emission tomography and structural magnetic resonance imaging data, reconstructs a model of the patient's cortex, uses machine-learning techniques to generate probabilities for mild cognitive impairments for local cortical regions, uses machine-learning techniques to fuse the local diagnoses to generate a global diagnosis based on each imaging modality, then uses machine-learning techniques to fuse the modality-specific global diagnoses to generate a final global diagnosis.

Computer aided diagnosis system for mild cognitive impairment

Growing community of inventors

Mansoura, Egypt

Mohammed Elmogy