Method and system for computer aided detection of cancer

• US 20030231790A1
• Filed: 05/02/2003
• Published: 12/18/2003
• Est. Priority Date: 05/02/2002
• Status: Abandoned Application

• Current Assignee: Flinders Technologies Pty., Ltd.
• Original Assignee: Flinders Technologies Pty., Ltd.
• Inventors: Bottema, Murk Jan
• Application Number:
  US10/427,907
• Publication Number:
  US 20030231790A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A computerised method is described for analysing a medical image to detect the presence of a cancer having a radiographic density close to the radiographic density of normal tissue. The method includes processing the image so as to obtain feature measurements for plural features of different pixel neighbourhoods within a region of the image, each pixel neighbourhood including a pixel having a local minimum intensity value. The feature measurements are used to classify each pixel neighbourhood as one of plural neighbourhood categories. Classification information for each neighbourhood category is then processed to thereby calculate parameters for the region. At least one of the region parameters are used to predict the presence of a cancer.

Claims

1. A computerised method of analysing a medical image to detect the presence of a cancer having a radiographic density close to the radiographic density of normal tissue, the method including the steps of:

a. processing the image so as to obtain feature measurements for plural features of different pixel neighbourhoods within a region of the image, each pixel neighbourhood including a pixel having a local minimum intensity value;

b. using the feature measurements to classify each pixel neighbourhood as one of plural neighbourhood categories;

c. processing classification information for each neighbourhood category to thereby calculate parameters for the region; and

d. using at least one of the region parameters to predict the presence of a cancer.

2. A method according to claim 1 wherein processing the image includes:

a. processing intensity values for each pixel in a pixel set associated with a respective single pixel having a local minimum intensity value;

b. obtaining a statistical value for each pixel set so as to identify a neighbourhood boundary for each set; and

c. obtaining feature measurements for plural features of each neighbourhood defined by a neighbourhood boundary.

3. A method according to claim 1 wherein the neighbourhood classification information includes:

a. the total number of neighbourhoods in each category; and

b. the set of heights (H) of the neighbourhood boundaries of each neighbourhood in a particular neighbourhood category, the heights being relative to the respective local minimum intensity value.

4. A method according to claim 3 wherein the region parameters include:

a. a mean height for each neighbourhood category; and

b. a normalised category count for each neighbourhood category.

5. A method according to claim 4 wherein using at least one of the region parameters to predict the presence of cancer in the image includes comparing at least some of the region parameters of the image with equivalent region parameters for images in which cancer has been detected.

37. A computer readable memory encoded with data representing a computer program executable to make a computer execute a method according to claim 1.

6. A computerised method of analysing a medical image to detect the presence of a cancer having a radiographic density close to the radiographic density of normal tissue, the method including the steps of:

a. pre-processing the image to select a region, the region including a plurality of pixels, each pixel having an intensity value;

b. identifying pixels in the region having a local minimum intensity value;

c. for each identified pixel having a local minimum intensity value, identifying an associated pixel neighbourhood;

d. for each identified neighbourhood;

i. obtaining measurements for plural features of the neighbourhood; and

ii. using the feature measurements to classify the neighbourhood, thereby providing neighbourhood classification information;

e. processing the neighbourhood classification information to calculate parameters for the region; and

f. using at least one of the region parameters to predict the presence of a cancer.

7. A method according to claim 6 wherein the step of pre-processing the image to select a region may further include processing the image to correct non-linearities.

8. A method according to claim 7 wherein the correction of non-linearities includes modifying the intensity values for pixels in the image using a correction function to thereby provide modified image pixel intensity values.

9. A method according to claim 6 wherein the pre-processing of the image to select a region includes:

a. sub-sampling the image to provide a sub-sampled image;

b. thresholding the sub-sampled image using a selected threshold value, the thresholding providing a modified sub-sampled image;

c. selecting the largest connected component in the modified sub-sampled image;

d. up-sampling the modified sub-sampled image to provide an up-sampled image; and

e. dilating the up-sampled image using a dilation element.

10. A method according to claim 9 wherein sub-sampling includes replacing plural arrays of image pixels with a respective single pixel, each single pixel having an intensity value which is equal to the average of intensity value pixels in a respective array.

11. A method according to claim 10 wherein each array is a square array.

12. A method according to claim 11 wherein the square array is a 10×

10 array of pixels.

13. A method according to claim 9 wherein thresholding includes:

a. assigning a binary ‘

zero’

value to pixels in the sub-sampled image having an intensity value which is less than a selected threshold value; and

b. assigning a binary ‘

one’

value to pixels in the sub-sampled image having an intensity value above the threshold value.

14. A method according to claim 9 wherein up-sampling includes replacing each pixel in the modified sub-sampled image with an array of pixels.

15. A method according to claim 14 wherein the array is a square array.

16. A method according to claim 9 wherein modifying the up-sampled image includes using a dilation element such that pixels in the up-sampled image having a zero value within a zone defined by the dilation element are assigned a non-zero value if another pixel within the zone defined by the dilation element also has a non-zero value.

17. A method according to claim 16 wherein the dilation element is a circular dilation element having a predetermined radius.

18. A method according to claim 6 wherein identifying pixels in the region having a local minimum intensity value includes identifying single pixel local minima in an intensity surface of the region.

19. A method according to claim 18 wherein identifying pixels in the region having a local minimum intensity value entails processing the pixel intensity values to identify pixels in the region having an intensity value which is less than the intensity values of adjacently located pixels.

20. A method according to claim 6 wherein identifying a pixel neighbourhood for each pixel having a local minimum intensity value includes:

a. processing intensity values for plural pixel sets, each pixel set having one of several non-overlapping paths, each path being substantially concentric about the local minimum, and substantially equally spaced, wherein the processing provides a statistical value for each pixel set; and

b. processing each statistical value to identify a neighbourhood boundary.

21. A method according to claim 20 wherein the paths are substantially circular.

22. A method according to claim 21 wherein the statistical value is the average pixel intensity value for the pixels in a pixel set.

23. A method according to claim 22 wherein the processing of the statistical values to identify a neighbourhood boundary includes comparing statistical values from adjacent pixel sets so as to identify a minimum difference between the statistical value of adjacent pixel sets.

24. A method according to claim 23 wherein the neighbourhood boundary is the path having a statistical value which is different from the statistical value of a smaller adjacent path by an amount which is less than the minimum difference.

25. A method according to claim 24 wherein the plural feature measurements include:

a. a value which is representative of the height (H) of the neighbourhood boundary relative to the local minimum intensity value of a pixel neighbourhood;

b. a value (R) which is representative of the radius (R) of the circular path about the pixel having the local minimum intensity value

c. a value (S) which is representative of the symmetry (S) of an intensity surface formed using statistical values of the pixel sets; and

d. a value (B) which is representative of the intensity value of a background about the pixel neighbourhood.

26. A method according to claim 25 wherein the height is computed as a difference between the average pixel value for pixels located on the boundary and the pixel intensity value of the single pixel local minimum in the neighbourhood.

27. A method according to claim 25 wherein the symmetry is computed using an average squared difference between the local intensity surface and a local model of the intensity surface obtained by revolving a function of the statical values about the single pixel local minimum.

28. A method according to claim 25 wherein the background is computed using the statistical value of the neighbourhood boundary.

29. A method according to claim 6 wherein the classifying of a neighbourhood using the feature measurements includes:

a. categorising each identified neighbourhood into a neighbourhood category according to a comparison of a neighbourhood'"'"'s respective feature measurements with plural sets of predetermined feature criteria; and

b. for each neighbourhood which is categorised using a neighbourhood category, incrementing a category count for the respective neighbourhood category.

30. A method according to claim 25 wherein the classifying of a neighbourhood using the feature measurements includes:

a. categorising each identified neighbourhood into a neighbourhood category according to a comparison of a neighbourhood'"'"'s respective feature measurements with plural sets of predetermined feature criteria; and

b. for each neighbourhood which is categorised using a neighbourhood category, incrementing a category count for the respective neighbourhood category.

31. A method according to claim 30 wherein the plural sets of predetermined feature criteria include:

Ω

₁={H>

19, R=1, S<

150, B>

2100} 

 

a. Ω

₂={H>

38, R=2, S<

200, B>

2100}; and

 

 

b. Ω

₃={H>

76, R=3, S<

300, B>

2100} 

 

c.

wherein Ω

<sub>1, Ω

2</sub> and Ω

₃ are neighbourhood categories.

32. A method according to claim 6 wherein the neighbourhood classification information includes:

a. the total number of neighbourhoods in each category; and

b. the set of heights (H) of the neighbourhood boundaries of each neighbourhood in a particular neighbourhood category, the heights being relative to the respective local minimum intensity value.

33. A method according to claim 6 wherein the calculation of region parameters uses classification information obtained for at least one neighbourhood category.

34. A method according to claim 33 wherein the region parameters include:

a. a mean height for each neighbourhood category; and

b. a normalised category count for each neighbourhood category.

35. A method according to claim 6 wherein the prediction includes providing an indication of the likelihood that cancer exits in the image.

38. A computer readable memory encoded with data representing a computer program executable to make a computer execute a method according to claim 6.

36. A computerised method of analysing a digital mammogram to detect the presence of an invasive lobular carcinoma in human breast tissue, the method including the steps of:

a. processing an image file for the digital mammogram so as to obtain feature measurements for plural features of different pixel neighbourhoods within a region of the digital mammogram, each pixel neighbourhood including a pixel having a local minimum intensity value;

b. using the feature measurements to classify each pixel neighbourhood as one of plural neighbourhood categories;

c. processing classification information for each neighbourhood category to thereby calculate parameters for the region; and

d. using at least one of the region parameters to predict the presence of an invasive lobular carcinoma.

39. A computer readable memory encoded with data representing a computer program executable to make a computer execute a method according to claim 36.

40. A system for analysing a medical image to detect the presence of a cancer having a radiographic density close to close to the radiographic density of normal tissue, the system including:

a. a programmable computer;

b. computer software installed onto the programmed computer, the computer software enabling the programmed computer to;

process the image so as to obtain feature measurements for plural features of different pixel neighbourhoods within a region of the image, each pixel neighbourhood including a pixel having a local minimum intensity value;

use the feature measurements to classify each pixel neighbourhood as one of plural neighbourhood categories;

process classification information for each neighbourhood category to thereby calculate parameters for the region; and

use at least one of the region parameters to predict the presence of a cancer.