Anterior commissure and posterior commissure segmentation system and method

• US 8,270,698 B2
• Filed: 04/08/2009
• Issued: 09/18/2012
• Est. Priority Date: 09/24/2008
• Status: Active Grant

• Current Assignee: Merative US LP
• Original Assignee: Merge Healthcare Incorporated (International Business Machines Corporation)
• Inventors: Geiger, Paul Arthur
• Primary Examiner(s): Johns, Andrew W
• Assistant Examiner(s): GORADIA, SHEFALI DINESH
• Application Number:
  US12/420,660
• Publication Number:
  US 20100074495A1
• Time in Patent Office: 1,259 Days
• Field of Search: 382/100, 382128-134
• US Class Current: 382/131
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: G01R 33/546 Interface between the MR system and the user, e.g. for controlling the operation of the MR system or for the design of pulse sequences
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 3/06 Topological mapping of higher dimensional structures onto lower dimensional surfaces
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/68 of symmetry
• CPC Class Codes: G06T 7/75 involving models

Abstract

A computer-implemented system and method of determining anterior commissure (AC) and posterior commissure (PC) points in a volumetric neuroradiological image. The method includes determining, by a computer, a mid-sagittal plane estimate to extract a mid-sagittal plane image from the volumetric neuroradiological image, and AC and PC point estimates in the mid-sagittal plane image. The method further includes determining, by the computer, a refined mid-sagittal plane estimate from the AC and PC point estimates to extract a refined mid-sagittal plane image, the AC point from the refined mid-sagittal plane image, and the PC point from the refined mid-sagittal plane image and the AC point.

Claims

1. A computer-implemented method of determining anterior commissure (AC) and posterior commissure (PC) points in a volumetric neuroradiological image, the method comprising:

determining, by a computer, a mid-sagittal plane estimate to extract a mid-sagittal plane image from the volumetric neuroradiological image;

determining, by the computer, AC and PC point estimates in the mid-sagittal plane image;

determining, by the computer, a refined mid-sagittal plane estimate from the AC and PC point estimates to extract a refined mid-sagittal plane image;

determining, by the computer, the AC point from the refined mid-sagittal plane image; and

determining, by the computer, the PC point from the refined mid-sagittal plane image and the AC point.

2. The method of claim 1, wherein determining the mid-sagittal plane estimate comprises:

creating an edge mask of each of a plurality of axial image slices;

identifying a symmetry axis of each edge mask; and

fitting a plane to the symmetry axes.

3. The method of claim 2, further comprising equalizing brightness of the plurality of axial image slices prior to creating the edge mask.

4. The method of claim 2, wherein the plurality of axial image slices includes slices in a vertical range from a medulla to the top of a corpus calossum.

5. The method of claim 2, wherein identifying the symmetry axis of each edge mask comprises:

(a) identifying a tentative symmetry axis, the tentative symmetry axis defining first and second halves of the edge mask;

(b) reflecting each pixel of the first half of the edge mask over the tentative symmetry axis;

(c) scoring one point for each pixel which lands on the second half of the edge mask; and

(d) adding each point into a total score for the tentative symmetry axis;

the method further comprising, iterating acts (a), (b), (c), and (d) for each of a range of tentative symmetry axes; and

identifying the tentative symmetry axis with the highest total score as the symmetry axis.

6. The method of claim 2, wherein fitting a plane to the symmetry axes comprises use of a robust regression method.

7. The method of claim 1, wherein determining AC and PC point estimates comprises applying an active appearance model to the mid-sagittal plane image.

8. The method of claim 7, wherein the active appearance model is one of a brainstem and third ventricle region.

9. The method of claim 1, wherein determining the refined mid-sagittal plane estimate comprises:

reformatting a plurality of axial image slices onto a plane which is perpendicular to the mid-sagittal plane estimate and which passes through the AC and PC point estimates;

extracting an axial plane image;

cropping the axial plane image to a region including the AC and PC point estimates and at least a portion of a brain ventricle extending therebetween;

creating an edge mask of the cropped axial plane image; and

identifying a symmetry axis of the cropped axial plane image.

10. The method of claim 9, wherein the symmetry axis of the edge mask is identified by,

(a) identifying a tentative symmetry axis, the tentative symmetry axis defining first and second halves of the edge mask;

(b) reflecting each pixel of the first half of the edge mask over the tentative symmetry axis;

(c) scoring one point for each pixel which lands on the second half of the edge mask; and

(d) adding each point into a total score for the tentative symmetry axis;

the method further comprising, iterating acts (a), (b), (c), and (d) for each of a range of tentative symmetry axes; and

identifying the tentative symmetry axis with the highest total score as the symmetry axis.

11. The method of claim 1, wherein determining the AC point comprises:

projecting the AC point estimate onto the refined mid-sagittal plane image; and

identifying a brightness peak within a region surrounding the projected AC point estimate as the AC point.

12. The method of claim 1, wherein determining the PC point comprises:

projecting the PC point estimate onto the refined mid-sagittal plane image;

creating an edge mask of the refined mid-sagittal plane image;

identifying a point X within a region surrounding the projected PC point estimate, which lies on the edge mask, and for which a gradient of the refined mid-sagittal plane image is approximately parallel to a direction from the AC point to the point X;

obtaining a point Y by translating the point X along the direction from the AC point to the point X; and

identifying an intensity peak in a direction perpendicular to the direction from the AC point to Y as the PC point.

13. An image processing system configured to determine anterior commissure (AC) and posterior commissure (PC) points in a volumetric neuroradiological image, the system comprising:

a first estimator to determine a mid-sagittal plane approximation from the volumetric neuroradiological image;

a second estimator to determine AC and PC point approximations from the mid-sagittal plane approximation;

a first refining module to identify a mid-sagittal plane using the AC and PC point approximations;

a second refining module to identify the AC point from the mid-sagittal plane; and

a third refining module to identify the PC point from the mid-sagittal plane and the AC point.

14. The image processing system of claim 13 for implementation within a picture archiving and communications system (PACS).

15. The image processing system of claim 13, wherein the volumetric neuroradiological image is obtained from a database accessible via the internet.

16. The image processing system of claim 13 wherein the processing system is a component of a segmentation tool kit.

17. A non-transitory computer readable medium encoded with a plurality of processor executable instructions for identifying anterior commissure (AC) and posterior commissure (PC) points in a volumetric neuroradiological image, the instructions enabling the execution of a method comprising:

determining a mid-sagittal plane estimate to extract a mid-sagittal plane image from the volumetric neuroradiological image;

determining AC and PC point estimates in the mid-sagittal plane image;

determining a mid-sagittal plane from the AC and PC point estimates to extract a refined mid-sagittal plane image;

determining the AC point from the refined mid-sagittal plane image; and

determining the PC point from the refined mid-sagittal plane image and the AC point.

18. The non-transitory computer readable medium of claim 17, wherein the AC and PC points are input data used in interventional radiological equipment.

19. The non-transitory computer readable medium of claim 17, wherein the AC and PC points are input data used to merge images from a plurality of imaging modalities.