Patents by Inventor Kalpitkumar Gajera

Kalpitkumar Gajera 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).

  • Systems and method for automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers
    Patent number: 8811701
    Abstract: Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
    Type: Grant
    Filed: November 8, 2011
    Date of Patent: August 19, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Parmeshwar Khurd, Leo Grady, Ali Kamen, Mamadou Diallo, Kalpitkumar Gajera, Peter Gall, Martin Requardt, Berthold Kiefer, Clifford R. Weiss
  • Systems and Method for Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized Via Boosted Classifiers
    Publication number: 20130064439
    Abstract: Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
    Type: Application
    Filed: November 8, 2011
    Publication date: March 14, 2013
    Applicant: Siemens Corporation
    Inventors: Parmeshwar Khurd, Leo Grady, Ali Kamen, Mamadou Diallo, Kalpitkumar Gajera, Peter Gall, Martin Requardt, Berthold Kiefer, Clifford R. Weiss