Patents by Inventor Marek Suliga

Marek Suliga 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: 20120314903
    Abstract: The present invention relates to multi-dimensional filtering of ultrasound scan data for antialiasing or reconstruction for the purpose of re-sampling.
    Type: Application
    Filed: November 16, 2010
    Publication date: December 13, 2012
    Applicant: Advanced Medical Diagnostics Holdings S.A.
    Inventors: Chris Bore, Dror Nir, Rina Nir, Marek Suliga
  • Patent number: 8290223
    Abstract: A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.
    Type: Grant
    Filed: October 24, 2007
    Date of Patent: October 16, 2012
    Assignee: Agfa HealthCare N.V.
    Inventors: Marek Suliga, Piet Dewaele, Rudi Deklerck, Edgard Nyssen, Gert Behiels
  • Publication number: 20090257657
    Abstract: The present invention relates to a method for processing and presenting at least a first image (102) and a second image (103), these images being digital medical images. A first step in the method is performing image registration between the first image (102) and the second image (103) to generate a pixel-level mapping between both images. The registration takes into account a region-level correspondence between the first image (102) and the second image (103) A second step is presenting the first image (102) and the second image (103) simultaneously on a display (103). A third step is presenting a first magnification (104) of a region of interest (106) in the first image (102) and a second magnification (105) of a corresponding region (108) in the second image (103). The corresponding region (108) in the second image (103) being determined based on the pixel-level mapping.
    Type: Application
    Filed: April 8, 2009
    Publication date: October 15, 2009
    Inventors: Frederik TEMMERMANS, Rudi DEKLERCK, Marek SULIGA, Gert BEHIELS, Piet DEWAELE, Catherine BREUCQ, Johan DE MEY
  • Publication number: 20080101678
    Abstract: A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.
    Type: Application
    Filed: October 24, 2007
    Publication date: May 1, 2008
    Applicant: AGFA HEALTHCARE NV
    Inventors: Marek Suliga, Piet Dewaele, Rudi Deklerck, Edgard Nyssen, Gert Behiels