Patents by Inventor Ayman S. El-Baz

Ayman S. El-Baz has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200285714
    Abstract: Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.
    Type: Application
    Filed: July 9, 2018
    Publication date: September 10, 2020
    Applicant: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Ahmed Shalaby, Fahmi Khalifa, Islam Abdelmaksoud
  • Patent number: 10768259
    Abstract: There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustm
    Type: Grant
    Filed: October 15, 2018
    Date of Patent: September 8, 2020
    Assignee: ZAYED UNIVERSITY
    Inventors: Fatma Taher, Ayman S. El-Baz
  • Publication number: 20200203001
    Abstract: 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.
    Type: Application
    Filed: July 7, 2018
    Publication date: June 25, 2020
    Inventors: Ayman S. El-Baz, Ahmed Soliman, Moumen El-Melegy, Mohamed Abou El-Ghar
  • Patent number: 10667778
    Abstract: 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 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.
    Type: Grant
    Filed: September 14, 2017
    Date of Patent: June 2, 2020
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Ahmed Soliman, Fahmi Khalifa, Ahmed Shaffie, Neal Dunlap, Brian Wang
  • Publication number: 20200126221
    Abstract: 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.
    Type: Application
    Filed: October 21, 2019
    Publication date: April 23, 2020
    Applicant: UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC.
    Inventors: Ayman S. El-Baz, Fatmaelzahraa El-Gamal, Mohammed Elmogy, Gregory N. Barnes
  • Publication number: 20200116808
    Abstract: There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustm
    Type: Application
    Filed: October 15, 2018
    Publication date: April 16, 2020
    Inventors: Fatma TAHER, Ayman S. EL-BAZ
  • Publication number: 20200012761
    Abstract: Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.
    Type: Application
    Filed: July 9, 2018
    Publication date: January 9, 2020
    Applicant: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Ahmed Shalaby, Fahmi Khalifa, Islam Abdelmaksoud
  • Patent number: 10453569
    Abstract: 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.
    Type: Grant
    Filed: February 23, 2018
    Date of Patent: October 22, 2019
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Amy Dwyer, Rosemary Ouseph, Fahmi Khalifa, Ahmed Soliman, Mohamed Shehata
  • Publication number: 20190237186
    Abstract: 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.
    Type: Application
    Filed: February 22, 2019
    Publication date: August 1, 2019
    Inventors: Ayman S. El-Baz, Amy Dwyer, Ahmed Soliman, Mohamed Shehata, Hisham Abdeltawab, Fahmi Khalifa
  • Patent number: 10262414
    Abstract: Systems, methods, and computer program products for classifying a brain are disclosed. An embodiment method includes processing image data to generate segmented image data of a brain cortex. The method further includes generating a statistical analysis of the brain based on a three dimensional (3D) model of the brain cortex generated from the segmented image data. The method further includes using the statistical analysis to classify the brain cortex and to identify the brain as being associated with a particular neurological condition. According to a further embodiment, generating the 3D model of the brain further includes registering a 3D volume associated with the model with a corresponding reference volume and generating a 3D mesh associated with the registered 3D volume. The method further includes generating the statistical analysis by analyzing individual mesh nodes of the registered 3D mesh based on a spherical harmonic shape analysis of the 3D model.
    Type: Grant
    Filed: July 29, 2016
    Date of Patent: April 16, 2019
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Matthew J. Nitzken, Ayman S. El-Baz, Manuel F. Casanova
  • Publication number: 20180182482
    Abstract: 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.
    Type: Application
    Filed: February 23, 2018
    Publication date: June 28, 2018
    Inventors: Ayman S. El-Baz, Amy Dwyer, Rosemary Ouseph, Fahmi Khalifa, Ahmed Soliman, Mohamed Shehata
  • Patent number: 9928347
    Abstract: 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.
    Type: Grant
    Filed: April 1, 2015
    Date of Patent: March 27, 2018
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Amy Dwyer, Rosemary Ouseph, Fahmi Khalifa, Ahmed Soliman, Mohamed Shehata
  • Publication number: 20180070905
    Abstract: 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 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.
    Type: Application
    Filed: September 14, 2017
    Publication date: March 15, 2018
    Inventors: Ayman S. El-Baz, Ahmed Soliman, Fahmi Khalifa, Ahmed Shaffie, Neal Dunlap, Brian Wang
  • Publication number: 20170032520
    Abstract: Systems, methods, and computer program products for classifying a brain are disclosed. An embodiment method includes processing image data to generate segmented image data of a brain cortex. The method further includes generating a statistical analysis of the brain based on a three dimensional (3D) model of the brain cortex generated from the segmented image data. The method further includes using the statistical analysis to classify the brain cortex and to identify the brain as being associated with a particular neurological condition. According to a further embodiment, generating the 3D model of the brain further includes registering a 3D volume associated with the model with a corresponding reference volume and generating a 3D mesh associated with the registered 3D volume. The method further includes generating the statistical analysis by analyzing individual mesh nodes of the registered 3D mesh based on a spherical harmonic shape analysis of the 3D model.
    Type: Application
    Filed: July 29, 2016
    Publication date: February 2, 2017
    Inventors: Matthew J. Nitzken, Ayman S. El-Baz, Manuel F. Casanova
  • Patent number: 9230321
    Abstract: A computer aided diagnostic system and automated method classify a brain through modeling and analyzing the shape of a brain cortex, e.g., to detect a brain cortex that is indicative of a developmental disorder such as ADHD, autism or dyslexia. A model used in such analysis describes the shape of brain cortices in terms of spherical harmonics required to delineate a unit sphere corresponding to the brain cortex to a model of the brain cortex.
    Type: Grant
    Filed: March 15, 2013
    Date of Patent: January 5, 2016
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Matthew Nitzken, Manuel F. Casanova
  • Patent number: 9230320
    Abstract: A computer aided diagnostic system and automated method diagnose lung cancer through modeling and analyzing the shape of pulmonary nodules. A model used in such analysis describes the shape of pulmonary nodules in terms of spherical harmonics required to delineate a unit sphere corresponding to the pulmonary nodule to a model of the pulmonary nodule.
    Type: Grant
    Filed: March 15, 2013
    Date of Patent: January 5, 2016
    Assignee: University of Louisville Research Foundation, Inc.
    Inventors: Ayman S. El-Baz, Matthew Nitzken
  • Publication number: 20150286786
    Abstract: 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.
    Type: Application
    Filed: April 1, 2015
    Publication date: October 8, 2015
    Inventors: Ayman S. El-Baz, Amy Dwyer, Rosemary Ouseph, Fahmi Khalifa, Ahmed Soliman, Mohamed Shehata
  • Patent number: 9014456
    Abstract: A computer aided diagnostic system and automated method diagnose lung cancer through modeling and analyzing the visual appearance of pulmonary nodules. A learned appearance model used in such analysis describes the appearance of pulmonary nodules in terms of voxel-wise conditional Gibbs energies for a generic rotation and translation invariant second-order Markov-Gibbs random field (MGRF) model of malignant nodules with analytically estimated characteristic voxel neighborhoods and potentials.
    Type: Grant
    Filed: February 8, 2012
    Date of Patent: April 21, 2015
    Assignee: University of Louisville Research Foundation, Inc.
    Inventor: Ayman S. El-Baz
  • Patent number: 8731255
    Abstract: A computer aided diagnostic system and automated method diagnose lung cancer through tracking of the growth rate of detected pulmonary nodules over time. The growth rate between first and second chest scans taken at first and second times is determined by segmenting out a nodule from its surrounding lung tissue and calculating the volume of the nodule only after the image data for lung tissue (which also includes image data for a nodule) has been segmented from the chest scans and the segmented lung tissue from the chest scans has been globally and locally aligned to compensate for positional variations in the chest scans as well as variations due to heartbeat and respiration during scanning.
    Type: Grant
    Filed: November 5, 2009
    Date of Patent: May 20, 2014
    Assignee: University of Louisville Research Foundation, Inc.
    Inventor: Ayman S. El-Baz
  • Publication number: 20130294669
    Abstract: A computer aided image processing system and automated method to improve tagged magnetic resonance image data through modeling and analyzing the magnetic resonance image data using a linear combination of discrete Gaussians model and using a Markov-Gibbs random field model. The processed magnetic resonance images include reduced noise associated with the tags, augmented gradients across a tag profile and an amplified tag to background contrast as compared to the original tagged magnetic resonance images.
    Type: Application
    Filed: March 15, 2013
    Publication date: November 7, 2013
    Applicant: UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC.
    Inventors: Ayman S. El-Baz, Matthew Nitzken, Garth M. Beache