Patents by Inventor Fahmi Khalifa

Fahmi Khalifa 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: 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: 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
  • 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: 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