# US 6,724,924 B1 · Brightness and contrast invariant detection of vertebra pedicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 6,724,924 B1 - Document ID: US6724924B1 - Status: Granted - Kind: B1 - Application number: 09/638,121 - Filed: 2000-08-14 - Granted: 2004-04-20 - Earliest claimed: 2000-08-14 - Assignees: Siemens Corporate Research, Inc. - Inventors: Guo-Qing Wei; Jianzhong Qian; Helmuth Schramm - Web page: https://patentgrep.com/patent/US6724924B1 ## CPC - A61B 5/103: Measuring devices for testing the shape, pattern, {colour,} size or movement of the body or parts thereof, for diagnostic purposes (measuring instruments specially adapted for dentistry A61C19/04) - A61B 6/00: Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment (instruments measuring radiation intensity for application in the field of nuclear medicine, e.g. in vivo counting, G01T1/161) - G06T 7/74: {involving reference images or patches} - A61B 5/7232: {involving compression of the physiological signal, e.g. to extend the signal recording period} - A61B 5/726: {using Wavelet transforms} - G06T 2207/30008: Bone - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A system and method for detecting pedicle positions in an image, in accordance with the present invention, includes providing a set of feature prototypes for a plurality of pedicle positions and orientations, providing an input image to be analyzed for pedicle positions and orientations, and determining intensity curvatures for a pedicle in the input image. The intensity curvatures are transformed to determine a feature vector for the pedicle in the input image. The feature vector is correlated to the feature prototypes to determine most likely positions and orientations of the pedicle. ## Claims ### Claim 1 A method for detecting pedicle positions in an image, comprising the steps of: providing a set of feature prototypes for a plurality of pedicle positions and orientations; providing an input image to be analyzed for pedicle positions and orientations; determining intensity curvatures for a pedicle in the input image; transforming the intensity curvatures to determine a feature vector for the pedicle in the input image; and correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle. ### Claim 2 (depends on 1) The method as recited in claim 1, wherein the image includes an X-ray image. ### Claim 3 (depends on 1) The method as recited in claim 1, wherein the step of providing a set of feature prototypes includes the steps of: selecting images of the pedicle in different positions and orientations; determining intensity curvatures of the images; transforming the intensity curvatures to determine features for the pedicle for each orientation and position; and performing an eigenvalue decomposition to provide the set of feature prototypes. ### Claim 4 (depends on 3) The method as recited in claim 3, wherein the step of transforming the intensity curvatures to determine features for the pedicle for each orientation and position includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 5 (depends on 1) The method as recited in claim 1, wherein the step of transforming the intensity curvatures to determine a feature vector for the pedicle in the input image includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 6 (depends on 1) The method as recited in claim 1, wherein the step of correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle includes the steps of: projecting the feature vector to the feature prototypes to create a reconstructed feature set; and comparing the reconstructed feature set to the feature vector to determine the most likely positions and orientations of the pedicle. ### Claim 7 (depends on 6) The method as recited in claim 6, further comprising the step of repeating the comparing step for a plurality of different image resolutions. ### Claim 8 (depends on 1) The method as recited in claim 1, further comprising the step of marking position points on the input image. ### Claim 9 A method for detecting pedicle positions in an image of a spine, comprising the steps of: training a set of feature prototypes for a plurality of pedicle positions and orientations by: selecting images of pedicles in different positions and orientations; determining intensity curvatures of the images; transforming the intensity curvatures to determine features for the pedicles for each orientation and position; and performing an eigenvalue decomposition to provide a set of feature prototypes; providing an input image to be analyzed for pedicle positions and orientations; and detecting pedicle locations and orientations by: determining intensity curvatures for the input image of the pedicle in the input image; transforming the intensity curvatures to determine a feature vector for the pedicle in the input image; and correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle. ### Claim 10 (depends on 9) The method as recited in claim 9, wherein the step of transforming the intensity curvatures to determine features for the pedicles for each orientation and position includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 11 (depends on 9) The method as recited in claim 9, wherein the step of transforming the intensity curvatures to determine a feature vector for the pedicle in the input image includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 12 (depends on 9) The method as recited in claim 9, wherein the step of correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle includes the steps of: projecting the feature vector to the feature prototypes to create a reconstructed feature set; and comparing the reconstructed feature set to the feature vector to determine the most likely positions and orientations of the pedicle. ### Claim 13 (depends on 12) The method as recited in claim 12, further comprising the step of repeating the comparing step for a plurality of different image resolutions. ### Claim 14 (depends on 9) The method as recited in claim 9, further comprising the step of marking position points on the input image. ### Claim 15 A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for detecting pedicle positions in an image, the method steps comprising: providing a set of feature prototypes for a plurality of pedicle positions and orientations; providing an input image to be analyzed for pedicle positions and orientations; determining intensity curvatures for a pedicle in the input image; transforming the intensity curvatures to determine a feature vector for the pedicle in the input image; and correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle. ### Claim 16 (depends on 15) The program storage device as recited in claim 15, wherein the image includes a digital X-ray image. ### Claim 17 (depends on 15) The program storage device as recited in claim 15, wherein the step of providing a set of feature prototypes includes the steps of: selecting images of the pedicle in different positions and orientations; determining intensity curvatures of the images; transforming the intensity curvatures to determine features for the pedicle for each orientation and position; and performing an eigenvalue decomposition to provide the set of feature prototypes. ### Claim 18 (depends on 17) The program storage device as recited in claim 17, wherein the step of transforming the intensity curvatures to determine features for the pedicle for each orientation and position includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 19 (depends on 15) The method as recited in claim 15, wherein the step of transforming the intensity curvatures to determine a feature vector for the pedicle in the input image includes employing one of a discrete cosine transformation and a wavelet transformation. ### Claim 20 (depends on 15) The program storage device as recited in claim 15, wherein the step of correlating the feature vector to the feature prototypes to determine most likely positions and orientations of the pedicle includes the steps of: projecting the feature vector to the feature prototypes to create a reconstructed feature set; comparing the reconstructed feature set to the feature vector to determine the most likely positions and orientations of the pedicle. ### Claim 21 (depends on 20) The program storage device as recited in claim 20, further comprising the step of repeating the comparing step for a plurality of different image resolutions. ### Claim 22 (depends on 15) The program storage device as recited in claim 15, further comprising the step of marking position points on the input image.