Method for producing result images for an examination object

• US 20050148852A1
• Filed: 12/08/2004
• Published: 07/07/2005
• Est. Priority Date: 12/08/2003
• Status: Abandoned Application

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Tank, Martin
• Application Number:
  US11/006,773
• Publication Number:
  US 20050148852A1
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4504 Bones A61B5/4547 takes precedence
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 8/00 Diagnosis using ultrasonic, sonic or infrasonic waves imaging of objects using sonar G01S15/00
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

A method is for automatically producing result images for an examination object using section image data. In this case, a target structure is first of all ascertained in the section image data on the basis of a diagnostic questionnaire, and the target structure is taken as a basis for selecting an anatomical norm model whose geometry can be varied using model parameters. The norm model is automatically adapted to the target structure. The section image data are then segmented on the basis of the adapted norm model, with anatomical structures of the examination object which are relevant to the diagnostic questionnaire being separated by selecting all of the pixels within the section image data which are situated within a contour of the adapted norm model and/or at least one model part in line with the relevant structures or have a maximum discrepancy therefrom by a particular value. The relevant structures are then visually displayed separately and/or are stored for later visual display. The document also describes a corresponding image processing system.

Claims

1. A method for automatically producing result images for an examination object using section image data from the examination object, comprising:

ascertaining a target structure in the section image data on the basis of a diagnostic questionnaire;

using the target structure as a basis for selecting an anatomical norm model with geometry variable using model parameters;

automatically adapting the norm model to the target structure in the section image data;

segmenting the section image data on the basis of the adapted norm model, with anatomical structures of the examination object which are relevant to the diagnostic questionnaire being separated by selecting all of the pixels within the section image data which are either situated within a contour of at least one of the adapted norm model and at least one model part in line with the relevant anatomical structures, or have a maximum discrepancy therefrom by a particular difference value; and

at least one of visually displaying the relevant anatomical structures separately and storing the relevant anatomical structures for later visual display.

2. The method as claimed in claim 1, wherein during the adaptation, a particular discrepancy function is respectively taken as a basis for ascertaining a current discrepancy value between the modified norm model and the target structure.

3. The method as claimed in claim 2, wherein the model parameters are altered in an automatic adaptation method such that the discrepancy value is minimized.

4. The method as claimed in claim 2, wherein the segmentation is preceded by an automatic check to determine whether adapting the norm model to the target structure involves a minimum discrepancy value being reached which is below a prescribed threshold value and the method otherwise being aborted for the purpose of further manual processing of the section image data.

5. The method as claimed in claim 1, wherein at least one separate anatomical structure of the examination object is automatically checked for discrepancies from the norm.

6. The method as claimed in claim 5, wherein ascertained discrepancies from the norm are at least one of visually displayed graphically and signaled to a user audibly with the associated separate anatomical structure.

7. The method as claimed in claim 5, wherein the examination object is automatically classified on the basis of ascertained discrepancies from the norm.

8. The method as claimed in claim 1, wherein the norm model is adapted in a plurality of iteration steps to the target structure in the section image data using model parameters which are in a hierarchical order in terms of their influence on the overall anatomical geometry of the model, and wherein the number of settable model parameters is increased in line with their hierarchical order as the number of iteration steps increases.

9. The method as claimed in claim 8, wherein the model parameters are respectively associated with one hierarchical class.

10. The method as claimed in claim 9, wherein a model parameter is associated with a hierarchical class on the basis of a discrepancy in the model geometry which arises when the model parameter in question is altered by a particular value.

11. The method as claimed in claim 10, wherein various hierarchical classes include particular value ranges of discrepancies associated with them.

12. The method as claimed in claim 1, wherein the norm models used are surface models generated on a triangular basis.

13. The method as claimed in claim 1, wherein the model parameters are respectively linked to a position for at least one anatomical landmark such that the model has an anatomically meaningful geometry for each parameter set.

14. The method as claimed in claim 1, wherein the target structure in the section image data is ascertained at least partly automatically using a contour analysis method.

15. A computer program product which can be loaded directly into a memory in a programmable image processing system, having program codes, in order to perform all of the steps of a method as claimed in claim 1 when the program product is executed on the image processing system.

18. The method as claimed in claim 3, wherein the segmentation is preceded by an automatic check to determine whether adapting the norm model to the target structure involves a minimum discrepancy value being reached which is below a prescribed threshold value and the method otherwise being aborted for the purpose of further manual processing of the section image data.

19. The method as claimed in claim 6, wherein the examination object is automatically classified on the basis of ascertained discrepancies from the norm.

20. The method as claimed in claim 2, wherein at least one separate anatomical structure of the examination object is automatically checked for discrepancies from the norm.

21. The method as claimed in claim 20, wherein ascertained discrepancies from the norm are at least one of visually displayed graphically and signaled to a user audibly with the associated separate anatomical structure.

22. The method as claimed in claim 20, wherein the examination object is automatically classified on the basis of ascertained discrepancies from the norm.

23. A computer program product which can be loaded directly into a memory in a programmable image processing system, having program codes, in order to perform all of the steps of a method as claimed in claim 2 when the program product is executed on the image processing system.

16. An image processing system for automatically producing result images for an examination object using section image data from the examination object, comprising:

an interface for receiving the measured section image data;

a target-structure ascertainment unit for ascertaining a target structure in the section image data on the basis of a diagnostic questionnaire;

a memory device having a number of anatomical norm models for various target structures in the section image data, whose geometry may respectively be varied on the basis of particular model parameters;

a selection unit for selecting one of the anatomical norm models in line with the ascertained target structure;

an adaptation unit for adapting the selected norm model to the target structure in the section image data;

a segmentation unit for segmenting the section image data on the basis of the adapted norm model and, in so doing, separating anatomical structures of the examination object which are relevant to the diagnostic questionnaire by selecting all of the pixels within the section image data which at least one of are situated within a contour of the adapted norm model or a model part in line with the relevant anatomical structures and have a maximum discrepancy therefrom by a particular difference value; and

a visual display unit for at least one of automatically visually displaying the relevant anatomical structures separately and storing the relevant anatomical structures for later visual display.

17. A modality for measuring section image data for an examination object, comprising an image processing system as claimed in claim 16.