Systems and methods for automatic symmetry identification and for quantification of asymmetry for analytic, diagnostic and therapeutic purposes

• US 20080021502A1
• Filed: 02/12/2007
• Published: 01/24/2008
• Est. Priority Date: 06/21/2004
• Status: Abandoned Application

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Inventors: Liu, Xin, D'Ambrosio, Anthony, Imielinska, Celina, Sughrue, Michael
• Application Number:
  US11/706,128
• Publication Number:
  US 20080021502A1
• Time in Patent Office: Days
• US Class Current: 607/1
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/501 for diagnosis of the head, e.g. neuroimaging or craniography
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/68 of symmetry

Abstract

Methods and related computational techniques are presented for analyzing images from various scan modalities, such as computerized tomography (“CT”). The methods convert image values to relative differences, to highlight side-to-side asymmetry. The conversion may be performed by comparing a small region of the scan to the corresponding region in the contralateral hemisphere, quantifying the degree of relative difference using statistical techniques, and representing this quantity of relative difference in a two dimensional or three dimensional relative difference map. In exemplary embodiments the method involves assigning an axis or plane of symmetry to a medical image, computing, using the image data, at least one relative difference map based on a comparison of two substantially symmetrical areas around the axis of symmetry, and generating a representation of any relative difference between the two symmetrical areas. In exemplary embodiments of the present invention the 3D orientation of a volumetric representation of an organ or anatomical area can be realigned within a scanner co-ordinate system. An inertia matrix can be computed on the sampled organ or structure, and principal axes can be can be derived from the eigenvectors of the inertia matrix. In alternate exemplary embodiments of the present invention, a volume, such as, for example, of a brain, can be represented as a re-parameterized surface point cloud. The interior contents can be removed, thus decomposing a symmetry plane computation problem into a surface matching routine. A search for a best matching surface can be implemented in a multi-resolution paradigm so as to optimize computational time. Subsequent to processing using either technique, in exemplary embodiments of the present invention a spatial affine transform can be applied to rotate the 3D images and align them within the co-ordinate system of the scanner. The corrected organ volume, for example, a brain, can then be re-sliced such that each planar image represents the organ at the same axial level.

Claims

16. A computer readable medium storing program code which, when executed, causes a computer to perform a method for evaluating a medical image represented by image data, the method comprising:

assigning an axis of symmetry to a medical image;

computing, using the image data, at least one relative difference map based on a comparison of two substantially symmetrical areas around the axis of symmetry; and

generating a representation of any difference between the two symmetrical areas.

17. A computer readable medium storing a data structure representing a relative difference map, the data structure comprising a quantification of statistical differences between image data values taken from corresponding value windows located substantially symmetrically with respect to an assigned axis of symmetry in a medical image.

18. A method for evaluating the symmetry of an image represented by image data, comprising:

computing a shape of a substantially symmetrical object of interest based on image data, the object of interest having at least two substantially symmetrical sections;

assigning an axis of symmetry to the object of interest such that the axis lies between the two substantially symmetrical sections;

optionally converting the shape of the object of interest to a substantially rectangular or square shape;

optionally normalizing the converted shape;

determining, using the image and shape information, a degree of symmetry between the at least two substantially symmetrical sections with respect to the axis of symmetry; and

generating a graphical representation of any difference between the two substantially symmetrical sections.

19. The method of claim 18 wherein said computing further comprises using a bounding function to compute the shape of the substantially symmetrical object of interest.

20. The method of claim 18 wherein said determining further comprises performing a pixel comparison of the image and shape information to determine the degree of symmetry.

21. The method of claim 18 wherein said computing further comprises using a Fourier shape descriptor to compute the shape of the substantially symmetrical object of interest.

22. The method of claim 18 wherein said assigning further comprises computing at least one centroid to define the axis of symmetry.

26. The method of claim 22, wherein after resampling a series of unique cubic polynomials is fitted between each of the data points.

23. A method of automatically identifying a plane of symmetry and correcting 3D orientation of volumetric images, comprising:

transforming a volume into a binary volume;

resampling the volume at a higher resolution;

computing a centroid and a covariance matrix of the volume;

forming an inertia matrix from the covariance matrix;

deriving principle axes of the volume form the inertia matrix; and

using the eigenvectors of the inertia matrix to obtain rotational angles of the mid-saggital plane of the volume.

24. The method of claim 23, wherein the volume is a volumetric image of a mammalian brain.

25-1. The method of claim 25, wherein re-slicing is conducted on a re-centered and reoriented volume.

27. A method, comprising:

representing a volumetric object as a re-parameterized surface point cloud;

removing the interior contents of the object; and

implementing a search for a best matching surface in a multi-resolution paradigm.

28. The method of claim 27, wherein the volume is a volumetric image of a mammalian brain.

29. The method of claim 27, wherein each location in the re-parameterized surface point cloud is parameterized by its elevation, azimuth and longitude.

30. The method of claim 27, further comprising performing an affine spatial transformation for tilt correction after the principal axes have been derived.

31. The method of claim 27, wherein re-slicing is conducted on a re-centered and reoriented volume.

32. A system for automatically identifying a plane of symmetry and correcting 3D orientation of volumetric images, comprising:

a binarizing module for transforming volume into a binary volume;

a resampling module for resampling the volume at a higher resolution;

a first computation module for computing a centroid and a covariance matrix of the volume;

an inertial matrix module for forming an inertia matrix from the covariance matrix and deriving principle axes of the volume form the inertia matrix; and

a second computational module for using the eigenvectors of the inertia matrix to obtain rotational angles of the mid-saggital plane of the volume.

33. A volumetric image analysis system, comprising:

a re-parameterization module for representing a volumetric object as a re-parameterized surface point cloud;

a de-interiorization module for removing the interior contents of the object; and

a surface matching module for implementing a search for a best matching surface in a multi-resolution paradigm.

34. The method of claim 33, wherein after said fitting of polynomials the volume is downsampled.