University of Louisville Research Foundation

Other

University of Massachusetts

The Ohio State University

Ahmed Soliman

Ayman Sabry El-Baz

Fahmi Khalifa

Harpal Sandhu

Giorgio Rizzoni

Amy Dwyer

Mohamed Shehata

Robert S Keynton

Sanket S Amberkar

Brian Thomas Murray

Ahmed Shalaby

Rosemary Ouseph

Ahmed Sharafeldeen

Mohamed Elsharkawy

Pierluigi Pisu

Ali Mahmoud

Byungho Lee

Guruprasad Anapathur Giridharan

Shlomit Schaal

Neal Dunlap

Mohammed Elmogy

Ahmed Sleman

Andrew Switala

Ahmed Eltanboly

Nabila Eldawi

Yann Guezennec

Brian Wang

Moumen El-Melegy

Mohamed Abou El-Ghar

Ahmed Shaffie

Hisham Abdeltawab

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.

Assessment of pulmonary function in coronavirus patients

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Segmentation of medical images

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 method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

Atlas for automatic segmentation of retina layers from OCT images

A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.

Computer aided diagnosis system for classifying kidneys

A system and computation method is disclosed that identifies radiation-induced lung injury after radiation therapy using 4D computed tomography (CT) scans. After deformable image registration, the method segments lung fields, extracts functional and textural features, and classifies lung tissues. The deformable registration locally aligns consecutive phases of the respiratory cycle using gradient descent minimization of the conventional dissimilarity metric. Then an adaptive shape prior, a first-order intensity model, and a second-order lung tissues homogeneity descriptor are integrated to segment the lung fields. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.

Accurate detection and assessment of radiation induced lung injury based on a computational model and computed tomography imaging

A computer aided diagnostic system and automated method to classify a kidney. Image data for a medical scan that includes image data of a kidney may be received. The kidney image data may be segmented from other image data of the medical scan. One or more iso-contours may be registered for the kidney image data, and renal cortex image data may be segmented from the kidney image data based on the one or more registered iso-contours. The kidney may be classified by analyzing one or more features determined from the segmented renal cortex image data using a learned model associated with the one or more features.

Computer aided diagnostic system for classifying kidneys

Torque estimation techniques in the real-time basis for engine control and diagnostics applications using the measurement of crankshaft speed variation are disclosed. Two different torque estimation approaches are disclosed&#x2014;'Stochastic Analysis' and 'Frequency Analysis.' An estimation model function consisting of three primary variables representing crankshaft dynamics such as crankshaft position, speed, and acceleration is used for each estimation approach. The torque estimation method are independent of the engine inputs (air, fuel, and spark). Both approaches have been analyzed and compared with respect to estimation accuracy and computational requirements, and feasibility for the real-time engine diagnostics and control applications. Results show that both methods permits estimations of the indicated torque based on the crankshaft speed measurement while providing not only accurate but also relatively fast estimations during the computation processes.

Engine control using torque estimation

A model-based Fault Detection and Isolation (FDI) system and method based on a hierarchical structure for monitoring overall vehicle system performance and diagnosing faults is disclosed. The FDI scheme uses the available sensors in a vehicle system and divides them into subsystems of smaller dimensions containing one or more modules that are related or interconnected. The same module may appear in a different subsystem, but the set of all subsystems does not have to contain all of the modules. For this structure, an FDI scheme comprising several detector units is created. Each detector unit receives information from the sensors and outputs a residual that is sent to a residual evaluation unit which processes the data and performs the residual evaluation for the selected subsystem. Finally, each subsystem outputs a decision that is sent to a supervisor unit performing the final diagnosis.

Model-based fault detection and isolation system and method

A model-based Fault Detection and Isolation (FDI) method and system for monitoring the overall performance in a vehicle system based on a hierarchical structure is disclosed. The FDI scheme uses the available sensors in a vehicle system and divides them into subsystems of smaller dimensions containing one or more modules that are related or interconnected. The same module may appear in a different subsystem, but the set of all subsystems does not have to contain all of the modules. For this structure, an FDI scheme comprising several detector units is created. Each detector unit receives information from the sensors and outputs a residual that is sent to a high-level detector unit which processes the data and performs the residual evaluation for the selected subsystem. Finally, each subsystem outputs a decision that is sent to a supervisor hazard detector performing the final diagnosis.

Fault detection and isolation system and method

Growing community of inventors

Louisville, KY, United States of America

Ahmed Soliman