Feature quantification from multidimensional image data

• US 7,333,648 B2
• Filed: 06/04/2004
• Issued: 02/19/2008
• Est. Priority Date: 11/19/1999
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Yavuz, Mehmet, Ishaque, Ahmad Nadeem, Edic, Peter Michael
• Primary Examiner(s): Mehta; Bhavesh M
• Assistant Examiner(s): KIM, CHONG R
• Application Number:
  US10/862,101
• Publication Number:
  US 20040223636A1
• Time in Patent Office: 1,355 Days
• Field of Search: None
• US Class Current: 382/131
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: A61B 6/541 involving acquisition triggered by a physiological signal
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

Techniques, hardware, and software are provided for quantification of extensional features of structures of an imaged subject from image data representing a two-dimensional or three-dimensional image. In one embodiment, stenosis in a blood vessel may be quantified from volumetric image data of the blood vessel. A profile from a selected family of profiles is fit to selected image data. An estimate of cross sectional area of the blood vessel is generated based on the fit profile. Area values may be generated along a longitudinal axis of the vessel, and a one-dimensional profile fit to the generated area values. An objective quantification of stenosis in the vessel may be obtained from the area profile. In some cases, volumetric image data representing the imaged structure may be reformatted to facilitate the quantification, when the structural feature varies along a curvilinear axis. A mask is generated for the structural feature to be quantified based on the volumetric image data. A curve representing the curvilinear axis is determined from the mask by center-finding computations, such as moment calculations, and curve fitting. Image data are generated for oblique cuts at corresponding selected orientations with respect to the curvilinear axis, based on the curve and the volumetric image data. The oblique cuts may be used for suitable further processing, such as image display or quantification.

Claims

1. A method for assessing an imaged subject, the method comprising the steps of:

obtaining imaging data of said imaged subject, said imaging data being representative of an extensional feature of said imaged subject;

applying direct quantitative analysis to said imaging data to determine at least one parameter value of said extensional feature, and

generating an output signal representative of the determined parameter value of said extensional feature;

the step of applying quantitative analysis further comprising the steps of selecting an imaging data set corresponding to a selected region of said imaged subject, identifying a spatially varying function, that is representative of the cross section of extensional feature in said imaging data set, the spatially varying function comprising non-circular functions and families of profiles, said profiles comprising Gaussian functions and ellipsoid functions and generating said at least one parameter value from the identified spatially varying function.

2. The method of claim 1 wherein the step of identifying a spatially varying function that is representative of the selected imaging data set further comprises the step of profile fitting the selected imaging data set with respective ones of spatially varying functions.

3. The method of claim 2 wherein the step of profile fitting further comprises coincidence fitting pixel brightness data in said selected imaging data with a selected one of said spatially varying functions and determining a best fit profile.

4. The method of claim 1 wherein the step of generating said at least one parameter value from the identified spatially varying function comprises determining the area encompassed by the identified spatially varying function.

5. The method of claim 4 wherein said imaged subject comprises an animal or human body and said extension feature comprises a vessel within said body.

6. The method of claim 5 wherein said at least one parameter value comprises the cross-sectional area of said vessel.

7. The method of claim 5 wherein the selected imaging data set represents a view of said imaged subject selected from the group of views orthogonal to the longitudinal axis of said vessel and views oblique to the longitudinal axis of said vessel.

8. The method of claim 1 further comprising the step of iteratively applying said quantitative analysis to respective selected imaging data sets so as to generate a plurality of respective parameter values for said imaging data sets.

9. The method of claim 1 further comprising the step of preprocessing said image data so as to enhance the contrast between the extensional feature and other portions of said imaging data.

10. The method of claim 1 further comprising the steps of computing background estimates and applying said background estimates in determination of said at least one parameter value.

11. The method of claim 1 wherein the step of obtaining imaging data comprises methods of generating imaging data selected from the groups of tomographic radiography, computed tomography imaging, and magnetic resonance imaging.

12. A method for determining the lumen in a vessel in a body, the method comprising the steps of:

obtaining imaging data of said vessel comprising a plurality of imaging data sets representative of respective imaging slices through said vessel;

determining a three-dimensional representation of the longitudinal axis of said vessel;

computing an orthogonal cross section data set for each of a plurality of respective positions along said longitudinal axis of said vessel;

wherein the step of determining a three-dimensional representation of the longitudinal axis of said vessel further comprises the steps of computing the lumen center point in selected ones of said imaging slices, wherein computing the lumen center point further comprises generating a mask for a region of interest encompassing said vessel;

computing a lumen area for each of said respective orthogonal cross section data sets; and

generating respective signals representative of said lumen areas associated with points along said longitudinal axis.

13. The method of claim 12 wherein the step of generating a mask for said region of interest comprises the steps of determining a seed pixel, performing threshold segmentation, and performing dilation operations.

14. The method of claim 13 wherein the step of computing the lumen center further comprises the step of performing a moment calculation on the binary representation of the bessel cross section provided by said mask.

15. The method of claim 12 wherein the step determining a three-dimensional representation of the longitudinal axis of said bessel further comprises fitting a three-dimensional curve to said plurality of successive lumen center points.

16. The method of claim 12 wherein the step of fitting a three dimensional curve comprises fitting a curve using least mean squared error metrics.

17. The method of claim 12 wherein the step computing a lumen area further comprises the steps of applying direct quantitative analysis to said orthogonal cross-sectional data set to determine said area;

the step of applying quantitative analysis further comprising the steps of identifying a spatially varying function that is representative of the cross section of said lumen in said orthogonal cross-section data.

18. The method of claim 17 wherein the said spatially varying functions comprise non-circular functions.

19. The method of claim 18 wherein said spatially varying functions comprise families of profiles, said profiles comprising Gaussian functions and ellipsoid functions.

20. The method of claim 17 wherein the step of identifying a spatially varying function that is representative of the cross section of said lumen further comprises the step of profile fitting the orthogonal cross-section data with respective ones of said spatially varying functions.

21. The method of claim 12 wherein said vessel is a blood vessel and said lumen area provides a representation of stenosis in said blood vessel.