Patents by Inventor Michael Kelm

Michael Kelm 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: 20140355854
    Abstract: A method and a segmentation system are disclosed. An embodiment of the method includes providing an image representation of the structure; providing a start surface model, including a mesh with a plurality of vertices connected by edges; defining for each vertex a ray normal to the surface model at the position of the vertex; assigning more than two labels to each vertex, each label representing a candidate position of the vertex on the ray; providing a representation of likelihoods for each candidate position the likelihood referring to whether the candidate position corresponds to a surface point of the structure in the image representation; and defining a first order Markow Random Field with discrete multivariate random variables, the random variables including the labels of the candidate positions and the representation of likelihoods, finding an optimal segmentation of the structure by using an maximum a posteriori estimation in this Markow Random Field.
    Type: Application
    Filed: January 7, 2014
    Publication date: December 4, 2014
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Michael KELM, Felix LUGAUER, Jingdan ZHANG, Yefeng ZHENG
  • Publication number: 20140254910
    Abstract: A method of assigning first localization data of a breast of a patient derived from first image data of the breast, the first image data being the result of a first radiological data acquisition process, to second localization data of the same breast derived from second image data, the second image data being the result of a second radiological data acquisition process, or vice versa. Thereby, the first localization data are assigned to the second localization data by intermediately mapping them into breast model data representing a patient-specific breast shape of the patient and then onto the second image data—or vice versa, thereby deriving assignment data. An assignment system performs the above-described method.
    Type: Application
    Filed: March 11, 2014
    Publication date: September 11, 2014
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: ANNA JEREBKO, MICHAEL KELM, MICHAEL SUEHLING, MICHAEL WELS
  • Publication number: 20140228667
    Abstract: A method in radiological imaging for determining lesions in image data of an examination object is described. In an embodiment, the method includes determining anatomical structures by hierarchical breakdown of the image data of the examination object. The method furthermore includes image data analysis for localizing lesion candidates in the anatomical structures. Moreover, the method also includes determining the lesions by evaluating and filtering the lesion candidates. Moreover, an image processing workstation in radiological imaging for determining lesions in image data of an examination object and an imaging apparatus are described.
    Type: Application
    Filed: February 10, 2014
    Publication date: August 14, 2014
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Peter DANKERL, Matthias HAMMON, Michael KELM, Michael SÜHLING, Alexey TSYMBAL, Michael WELS, Andreas WIMMER
  • Publication number: 20140219548
    Abstract: A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.
    Type: Application
    Filed: February 7, 2013
    Publication date: August 7, 2014
    Applicants: Siemens Aktiengesellschaft, Siemens Corporation
    Inventors: Michael Wels, Michael Kelm, Michael Suehling, Shaohua Kevin Zhou
  • Publication number: 20140185888
    Abstract: An embodiment of the method is disclosed for non-invasive lesion candidate detection in a patient's body includes generating a number of first medical images of the patient's body. The method further includes identifying lesion-like geometrical regions inside the first medical images of the patient's body by applying image processing methods, whereby the identification is at least partly controlled by a number of patient-specific context features which are not directly extractable from the first medical images. In addition, the method includes selecting a number of the identified lesion-like geometrical regions as lesion candidates.
    Type: Application
    Filed: December 4, 2013
    Publication date: July 3, 2014
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Michael KELM, Michael Sühling, Alexey TSYMBAL, Michael WELS
  • Patent number: 8744172
    Abstract: A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
    Type: Grant
    Filed: June 15, 2011
    Date of Patent: June 3, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Alexey Tsymbal, Michael Kelm, Maria Jimena Costa, Shaohua Kevin Zhou, Dorin Comaniciu, Yefeng Zheng, Alexander Schwing
  • Patent number: 8693750
    Abstract: A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
    Type: Grant
    Filed: January 3, 2012
    Date of Patent: April 8, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Michael Wels, Michael Suehling, Shaohua Kevin Zhou, David Liu, Dijia Wu, Christopher V. Alvino, Michael Kelm, Grzegorz Soza, Dorin Comaniciu
  • Patent number: 8526699
    Abstract: A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.
    Type: Grant
    Filed: March 4, 2011
    Date of Patent: September 3, 2013
    Assignee: Siemens Aktiengesellschaft
    Inventors: Sushil Mittal, Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Shaohua Kevin Zhou, Dorin Comaniciu, Michael Kelm, Alexey Tsymbal, Dominik Bernhardt
  • Publication number: 20120321174
    Abstract: A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
    Type: Application
    Filed: June 15, 2011
    Publication date: December 20, 2012
    Applicants: Siemens Aktiengesellschaft, Siemens Corporation
    Inventors: Alexey Tsymbal, Michael Kelm, Maria Jimena Costa, Shaohua Kevin Zhou, Dorin Comaniciu, Yefeng Zheng, Alexander Schwing
  • Publication number: 20120183193
    Abstract: A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
    Type: Application
    Filed: January 3, 2012
    Publication date: July 19, 2012
    Applicants: Siemens Aktiengesellschaft, Siemens Corporation
    Inventors: Michael Wels, Michael Suehling, Shaohua Kevin Zhou, David Liu, Dijia Wu, Christopher V. Alvino, Michael Kelm, Grzegorz Soza, Dorin Comaniciu
  • Publication number: 20110235887
    Abstract: A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
    Type: Application
    Filed: March 22, 2011
    Publication date: September 29, 2011
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Rüdiger Bertsch, Roland Brill, Alexander Cavallaro, Maria Jimena Costa, Martin Huber, Michael Kelm, Helmut König, Sascha Seifert, Michael Wels
  • Publication number: 20110224542
    Abstract: A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.
    Type: Application
    Filed: March 4, 2011
    Publication date: September 15, 2011
    Inventors: Sushil Mittal, Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Shaohua Kevin Zhou, Dorin Comaniciu, Michael Kelm, Alexey Tsymbal, Dominik Bernhardt
  • Publication number: 20110182493
    Abstract: A method and a system are disclosed for image annotation of images, in particular two- and three-dimensional medical images. In at least one embodiment, the image annotation system includes an image parser which parses images retrieved from an image database or provided by an image acquisition apparatus and segments each image into image regions. The image can be provided by any kind of image acquisition apparatus such as a digital camera an x-ray apparatus, a computer tomograph or a magnetic resonance scanning apparatus. Each segmented image regions is annotated automatically with annotation data and stored in an annotation database. In at least one embodiment, the system includes at least one user terminal which loads at least one selected image from said image database and retrieved the corresponding annotation data of all segmented image regions of said image from said annotation database for further annotation of the image.
    Type: Application
    Filed: February 24, 2010
    Publication date: July 28, 2011
    Inventors: Martin HUBER, Michael Kelm, Sascha Seifert
  • Patent number: 7949167
    Abstract: A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.
    Type: Grant
    Filed: April 22, 2009
    Date of Patent: May 24, 2011
    Assignees: Siemens Medical Solutions USA, Inc., Siemens Aktiengesellschaft
    Inventors: Arun Krishnan, Xiang Zhou, Martin Huber, Michael Kelm, Joerg Freund
  • Publication number: 20110116698
    Abstract: A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.
    Type: Application
    Filed: November 16, 2010
    Publication date: May 19, 2011
    Applicant: Siemens Corporation
    Inventors: Michael Weis, Michael Suehling, Michael Kelm, Sascha Seifert, Maria Jimena Costa, Alexander Cavallaro, Martin Huber, Dorin Comaniciu
  • Publication number: 20110064291
    Abstract: A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.
    Type: Application
    Filed: June 7, 2010
    Publication date: March 17, 2011
    Applicants: Siemens Corporation, Siemens Aktiengesellschaft
    Inventors: Michael Kelm, Shaohua Kevin Zhou, Yefeng Zheng, Michael Suehling
  • Publication number: 20090310836
    Abstract: A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.
    Type: Application
    Filed: April 22, 2009
    Publication date: December 17, 2009
    Applicants: Siemens Medical Solutions USA, Inc., Siemens Aktiengesellschaft
    Inventors: Arun Krishnan, Xiang Zhou, Martin Huber, Michael Kelm, Joerg Freund