Method and System for Automatic Detection and Segmentation of Tumors and Associated Edema (Swelling) in Magnetic Resonance (Mri) Images

• US 20080292194A1
• Filed: 04/27/2006
• Published: 11/27/2008
• Est. Priority Date: 04/27/2005
• Status: Abandoned Application

• Current Assignee: Alberta Cancer Foundation, The Governors of the University of Alberta (University of Alberta)
• Original Assignee: Alberta Cancer Foundation, The Governors of the University of Alberta (University of Alberta)
• Inventors: Schmidt, Mark, Greiner, Russell, Murtha, Albert D.
• Application Number:
  US11/912,864
• Publication Number:
  US 20080292194A1
• Time in Patent Office: Days
• US Class Current: 382/217
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4878 Evaluating oedema
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method and system for segmenting an object represented in one or more input images, each of the one or more input images comprising a plurality of pixels. The method comprising: aligning the one or more input images with one or more corresponding template images each comprising a plurality of pixels; extracting features of each of the one or more input images and one or more template images; and classifying each pixel, or a group of pixels, in the one or more input images based on the measured features of the one or more input images and the one or more corresponding template images in accordance with a classification model mapping image properties or features to a respective class so as to segment the object represented in the one or more input images according to the classification of each pixel or group of pixels.

Claims

1. In a data processing system, a method for segmenting an object represented in one or more input images, each of the one or more input images comprising a plurality of pixels, the method comprising the steps of:

aligning the one or more input images with one or more corresponding template images each comprising a plurality of pixels;

extracting features of each of the one or more input images and one or more template images; and

classifying each pixel, or a group of pixels, in the one or more input images based on the extracted features of the one or more input images and the one or more corresponding template images in accordance with a classification model mapping image properties or features to a respective class so as to segment the object represented in the one or more input images according to the classification of each pixel or group of pixels.

2. The method of claim 1, further comprising relaxing the classification of each pixel or group of pixels.

3. The method of claim 2, wherein the relaxing comprises reclassifying each pixel or group of pixels in the one or more input images in accordance with the classification or extracted features of other pixels in the one or more input images so as to take into account the classification or extracted features of the other pixels in the one or more input images.

4. The method of claim 2, wherein the relaxing comprises reclassifying each pixel or group of pixels in the one or more input images in accordance with the classification of surrounding pixels in the one or more input images so as to take into account the classification of the surrounding pixels in the one or more input images.

5. The method of claim 4, wherein the reclassifying comprises applying a spatial median filter over the classifications of each pixel or group of pixels such that the classification of each pixel is consistent with the classification of the surrounding pixels in the one or more input images.

6. The method of claim 1, wherein the extracted features are based on one or more pixels in the respective one or more input and template images.

7. The method of claim 1, wherein the extracted features are based on individual pixels in the respective one or more input and template images.

8. The method of claim 1, wherein the classification model defines a classification in which each pixel or group of pixels representing the object in the one or more input images is classified as belonging to one of two or more classes defined by the classification model.

9. The method of claim 1, wherein the classification model defines a binary classification in which each pixel or group of pixels representing the object in the one or more input images is classified as belonging to either a “

normal”

class or an “

abnormal”

class defined by the classification model.

10. The method of claim 1, wherein the features are one or more of:

image-based features based on measurable properties of the one or more input images or corresponding signals;

coordinate-based features based on measurable properties of a coordinate reference or corresponding signals;

registration-based features based on measurable properties of the template images or corresponding signals.

11. The method of claim 1, wherein the extracted features are image-based features based on measurable properties of the one or more input images;

coordinate-based features based on measurable properties of a coordinate reference; and

registration-based features based on measurable properties of the template images.

12. The method of claim 10, wherein the image-based features comprise one or more of:

intensity features, texture features, histogram-based features, and shape-based features.

13. The method of claim of claim 10, wherein the coordinate-based features comprises one or more of:

measurable properties of the coordinate reference;

spatial prior probabilities for structures or object subtypes in coordinate reference; and

local measures of variability within the coordinate reference.

14. The method of claim 10, wherein the one or more input images are medical images, the coordinate-based features comprising one or more of:

measurable properties of the coordinate reference, spatial prior probabilities for structures or tissue types in coordinate reference, and local measures of anatomic variability within the coordinate reference.

15. The method of claim 10, wherein the registration-based features comprises one or more of:

features based on identified regions in the template images;

measurable properties at the template images;

features derived from a spatial transformation of the one or more input images; and

features derived from a line of symmetry of the one or more template images.

16. The method of claim 1, further comprising, before the aligning step, the step of reducing intensity inhomogeneity within and/or between the one or more input images.

17. The method of claim 1, further comprising, before the aligning step, the step of reducing noise in the one or more input images.

18. The method of claim 16, wherein the step of reducing intensity inhomogeneity comprises one or more of the steps of:

two-dimensional noise reduction comprising reducing local noise within the input images;

inter-slice intensity variation reduction comprising reducing intensity variations between adjacent images in an image series formed by the input images;

intensity inhomogeneity reduction for reducing gradual intensity changes over the image series; and

three-dimensional noise reduction comprising reducing local noise between over the image series.

19. The method of claim 18, wherein the two-dimensional noise reduction comprises applying edge-preserving and/or edge-enhancing smoothing methods.

20. The method of claim 18, wherein the two-dimensional noise reduction comprises applying a two-dimensional Smallest Univalue Segment Assimilating Nucleus (SUSAN) filter to the images.

21. The method of claim 18, wherein the three-dimensional noise reduction comprises applying edge-preserving and/or edge-enhancing smoothing methods.

22. The method of claim 18, wherein the three-dimensional noise reduction comprises applying a three-dimensional SUSAN filter to the image series.

23. The method of claim 18, wherein the step of intensity inhomogeneity reduction comprises Nonparametric Nonuniform intensity Normalization (N3).

24. The method of claim 18, further comprising standardizing the intensity of the one or more input images.

25. The method of claim 24, wherein the intensity of the one or more input images is standardized relative to the template image intensities.

26. The method of claim 24, wherein the intensity of the input images is standardized collectively so as to increase a measured similarity between the one or more input images.

27. The method of claim 24, wherein the steps of two-dimensional noise reduction, inter-slice intensity variation reduction, intensity inhomogeneity reduction, three-dimensional noise reduction, and intensity standardization are performed sequentially.

28. The method of claim 1, wherein the step of aligning the one or more input images with one or more template images comprises:

spatially aligning the one or more input images with one or more corresponding template images in accordance within a standard coordinate system such that the object represented in the one or more input images is aligned with a template object in the one or more template images;

spatially transforming the one or more input images to increase correspondence in shape of the object represented in the one or more input images with the template object in the one or more template images; and

spatially interpolating the one or more input images so as that the pixels in the spatially transformed one or more input images have the same size and spatially correspond to the pixels in the one or more template images in accordance with the standard coordinate system.

29. The method of claim 1, wherein the steps of spatially aligning, spatially transforming, and spatially interpolating are performed sequentially.

30. The method of claim 28, further comprising, before spatially aligning the one or more input images with the one or more template images, spatially aligning and/or spatially transforming the one or more input images so to align the object represented in the one or more input images with each another.

31. The method of claim 1, wherein the one or more input images are images generated by a magnetic resonance imaging procedure or medical imaging procedure.

32. The method of claim 1, wherein the one or more input images include at least one of:

medical imaging images, magnetic resonance images, magnetic resonance T1-weighted images, magnetic resonance T2-weighted images, magnetic resonance spectroscopy images, and anatomic images.

33. The method of claim 1, wherein the object represented in the one or more input images is a visual representation of a brain, the classification model segmenting the visual representation of the brain into objects that include at least one of:

tumors, edema, lesions, brain tumors, brain edema, brain lesions, multiple sclerosis lesions, areas of stroke, and areas of brain damage.

34. The method of claim 1, wherein the one or more input images comprises an image series of cross-sectional images taken in a common plane and offset with respect to one another so as to represent a volume, the one or more input images being arranged in the image series so as to spatially correspond to the respective cross-sections of the volume.

35. The method of claim 1, further comprising presenting a visual representation of the classification of each pixel or group of pixels on a display of the data processing system.

36. The method of claim 1, wherein the visual representation is provided by colour-coding each pixel or group of pixels in accordance with its respective classification.

37. The method of claim 1, wherein the visual representation is provided by delineating each pixel or group of pixels in accordance with its respective classification.

38. The method of claim 1, further comprising outputting or transmitting a computer data signal containing computer-execute code for presenting a visual representation of the classification of each pixel or group of pixels on a display device.

39. The method of claim 1, wherein each pixel is classified separately.

41. A data processing system for segmenting an object represented in one or more input images, each of the one or more input images each comprising a plurality of pixels, the data processing system comprising:

a display, one or more input devices, a memory, and a processor operatively connected to the display, input devices, and memory;

wherein the memory having data and instructions stored thereon to configure the processor to;

perform the method of claim 1.

42. A computer-readable medium carrying data and instructions for programming a data processing system to perform the method of claim 1.

40. A data processing system for segmenting one or more input images into objects, each of the one or more input images each comprising a plurality of pixels, the data processing system comprising:

a display, one or more input devices, a memory, and a processor operatively connected to the display, input devices, and memory;

the memory having data and instructions stored thereon to configure the processor to;

align the one or more input images with one or more corresponding template images each comprising a plurality of pixels;

measure features of each of the one or more input images and one or more template images; and

classify each pixel, or a group of pixels, in the one or more input images based on the extracted features of the one or more input images and the one or more corresponding template images in accordance with a classification model mapping image properties or features to a respective class so as to segment the object represented in the one or more input images according to the classification of each pixel or group of pixels.

43. In a data processing system, a method for segmenting an object represented in one or more images, each of the one or more images comprising a plurality of pixels, the method comprising the steps of:

measuring image properties or extracting image features of the one or more images at a plurality of locations;

measuring image properties or extracting image features of one or more template images at a plurality of locations corresponding to the same locations in the one or more images, each of the template images comprising a plurality of pixels; and

classifying each pixel, or a group of pixels, in the one or more images based on the measured properties or extracted features of the one or more images and the one or more template images in accordance with a classification model mapping image properties or extracted features to respective classes so as to segment the object represented in the one or more images according to the classification of each pixel or group of pixels.