Initializing model-based interpretations of digital radiographs

• US 20040101186A1
• Filed: 11/27/2002
• Published: 05/27/2004
• Est. Priority Date: 11/27/2002
• Status: Abandoned Application

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Tong, Xin, Berestov, Alexander
• Application Number:
  US10/306,341
• Publication Number:
  US 20040101186A1
• Time in Patent Office: Days
• US Class Current: 382/132
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

Automated computer aided diagnosis (CAD) processing of digital radiographs through model-based interpretation, with the initialization providing a set of initial parameters used by the model. The initial parameters can be selected based on expected pathology in the digital radiograph, and are optimized by the model to match features shown in the radiograph. The model can be an iterative model or a non-iterative model. Analysis is performed on the interpretation result, so as to diagnose pathology shown in the radiograph.

Claims

1. A method for obtaining initial model parameters for a model-based interpretation of a digital radiograph obtained from a patient in accordance with a radiographic protocol, wherein the model-based interpretation changes the model parameters based on content of the digital radiograph so as to model features therein, said method comprising:

identifying a region of interest in the radiograph, wherein the region of interest is identified based on landmarks common to multiple different radiographs obtained with the same radiographic protocol; and

analyzing the region of interest so as to calculate one or more candidates for initial model parameters.

2. A method according to claim 1, wherein the landmarks comprise distinctive regions of high contrast within multiple different radiographic images produced by the same radiographic protocol.

3. A method according to claim 2, wherein the radiographic protocol is a lateral lumbar spine protocol, and wherein the landmarks include a bright pelvic area, a dark area corresponding to a non-patient region beyond the patient'"'"'s back, and a dark lung area, which is separated from the dark non-patient area by a bright spine comprising the region of interest.

4. A method according to claim 1, wherein said identifying step comprises image enhancement techniques including equalization, window leveling, and thresholding so as to define the region of interest.

5. A method according to claim 1, wherein the candidates for initial model parameters are calculated based on visually significant features in the region of interest together with spatial orientation of such features within the region of interest.

6. A method according to claim 5, wherein the radiographic protocol is a lateral lumbar spine examination, and the initial parameters for the deformable model define five nearly-identical rectangular regions corresponding to five vertebrae above the iliac bone, and the candidates for initial model parameters are calculated without regard to which of the five rectangular regions corresponds to one of the vertebrae.

7. A method according to claim 1, further comprising the step of disambiguating the candidates for initial model parameters with respect to repetitive structures found in the region of interest.

8. A method according to claim 7, wherein disambiguation is performed relative to the landmarks used to determine the region of interest.

9. A method according to claim 8, wherein disambiguation is also performed relative to boundaries of the region of interest itself.

10. A method according to claim 9, wherein the radiographic protocol is a lateral lumbar spine examination, and the initial model parameters are disambiguated by distance measurements relative to a dark region of the lung and a bright region of the iliac bone.

11. A method according to claim 1, further comprising the step of selecting an initial set of model parameters from among multiple different initial sets corresponding to multiple different pathologies.

12. A method according to claim 1, further comprising the steps of:

selecting multiple different models corresponding to multiple different pathologies;

obtaining an initial set of model parameters for each different model according to claim 1;

changing each initial set of model parameters according to the model-based interpretation; and

selecting one set of model parameters based on convergence of all models in the model-based interpretation.

13. A method according to claim 1, wherein the model-based interpretation is based on an iterative model.

14. A method according to claim 1, wherein the model-based interpretation is based on a non-iterative model.

15. Automated CAD processing of a digital radiograph through identification of a set of initial model parameters for a model-based interpretation of the digital radiograph according to any of claims 1 to 14, comprising:

changing model parameters according to the model-based interpretation so as to obtain a best estimate of features found within the radiograph; and

analysis of the interpretation results so as to provide computer assisted diagnosis of pathology found in the radiograph.