Computerized schemes for detecting and/or diagnosing lesions on ultrasound images using analysis of lesion shadows

• US 20030161513A1
• Filed: 02/22/2002
• Published: 08/28/2003
• Est. Priority Date: 02/22/2002
• Status: Abandoned Application

• Current Assignee: University of Chicago The (University of Chicago)
• Original Assignee: University of Chicago
• Inventors: Giger, Maryellen L., Drukker, Karen
• Application Number:
  US10/079,820
• Publication Number:
  US 20030161513A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 8/0833 involving detecting or locating foreign bodies or organic structures
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

Computerized detection and diagnostic schemes for sonographic images combine the benefits of computerized machine detection with the acquisition of non-radiographic medical images of special use for the screening of high risk, young patients who do not want the effects of ionizing characteristic of mammography. The lesion schemes employ computer-assisted interpretation of medical sonographic images, and output potential lesion sites and/or diagnosis of those lesions. More specifically, an embodiment of the computerized detection scheme involves convoluting a sonographic image with a mask of a given ROI (region of interest) size, and calculating a skewness value for each mask location, and assembling the calculated skewness values to form a skewness image. Thresholds are applied to pixels of the skewness image to determine potential shadows. (Ultrasound images show characteristic posterior acoustic behavior for different lesion types: Posterior acoustic shadowing is often observed for malignant lesions and for some benign solid masses, while posterior acoustic enhancement is often seen for cysts.) An embodiment of the diagnostic scheme (classifying a detected lesion as malignant or benign, for example) involves calculating the skewness of a shadow of a detected lesion, and comparing the calculated skewness to a threshold to arrive at a diagnosis. The detection and diagnostic schemes may also involve merging skewness values with other values determined in accordance with other analytic features, to arrive more comprehensive detection and diagnoses. The schemes are computationally efficient, allowing their use in real-time sonography.

Claims

1. A method of detecting at least a candidate abnormality in a sonographic image, the method comprising:

calculating plural skewness values at respective plural locations in the sonographic image; and

determining an area in the sonographic image to be the candidate abnormality, based at least in part on the skewness values.

2. The method of claim 1, further comprising:

merging the skewness values with other pixel values determined in accordance with other analytic features, so as to form plural merged pixels;

forming a merged image from the plural merged pixels; and

comparing the merged values in the merged image to a threshold, so as to arrive at comparison results that are used in the candidate abnormality area determining step.

3. The method of claim 2, wherein:

the other analytic features are derived from the sonographic image.

4. The method of claim 1, further comprising:

forming a skewness image from the plural skewness values; and

comparing the skewness values in the skewness image to a threshold, so as to arrive at comparison results that are used in the candidate abnormality area determining step.

5. The method of claim 4, wherein the calculating step comprises:

convoluting the sonographic image with a mask by moving the mask over plural locations in the sonographic image; and

calculating the plural skewness values at respective locations of the mask.

6. The method of claim 5, wherein the skewness image forming step comprises:

assigning the plural skewness values to respective mask center points.

7. The method of claim 4, wherein the candidate abnormality area determining step comprises:

determining a particular skewness value to indicate part of a candidate abnormality when the particular skewness value exceeds the threshold.

8. The method of claim 7, further comprising:

calculating a standard deviation of skewness values in the skewness image; and

determining the threshold as a mathematical function of the standard deviation.

9. The method of claim 8, wherein the threshold determining step comprises:

determining the threshold as being directly proportional to a first power of the standard deviation of the skewness values in the skewness image.

10. The method of claim 1, wherein the calculating step comprises:

calculating the skewness values as a mathematical function of a standard deviation of a gray-value distribution of pixels in the sonographic image.

11. The method of claim 10, wherein the calculating step comprises calculating the skewness values according to a formula:

12. The method of claim 1, further comprising:

superimposing an emphasis symbol on the sonographic image so as to indicate the area that was determined to be the candidate abnormality.

13. The method of claim 1, further comprising:

forming a histogram of gray values of pixels in the sonographic image to form a gray value histogram; and

adding white noise to the gray value histogram to form a modified gray value histogram that is configured for use in the skewness value calculating step.

14. The method of claim 1, further comprising:

repeatedly executing the steps of claim 1 to detect the candidate abnormality based on a sequence of sonographic images in real time.

15. An automated method of diagnosing a candidate abnormality in a sonographic image, the method comprising:

determining an area of the candidate abnormality in the sonographic image using the candidate abnormality detecting method of any of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14;

calculating an abnormality skewness value of the area that was determined to be the candidate abnormality; and

determining a likelihood of malignancy of the candidate abnormality based at least in part on the abnormality skewness value.

16. The method of claim 15, wherein the likelihood determining step comprises:

comparing the abnormality skewness value to a threshold; and

determining the candidate abnormality to be malignant if the abnormality skewness value exceeds the threshold, and to be benign if the abnormality threshold exceeds the abnormality skewness value.

17. A system implementing the method of claim 16.

18. A computer program product storing program instructions for execution on a computer system, which when executed by the computer system, cause the computer system to perform the method recited in claim 16.

19. A system implementing the method of claim 15.

20. A computer program product storing program instructions for execution on a computer system, which when executed by the computer system, cause the computer system to perform the method recited in claim 15.

23. A system implementing the method of any of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21 or 22.

24. A computer program product storing program instructions for execution on a computer system, which when executed by the computer system, cause the computer system to perform the method of any of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21 or 22.

21. A method of diagnosing a designated candidate abnormality in an area of a sonographic image, the method comprising:

calculating an abnormality skewness value of the area; and

determining a likelihood of malignancy of the candidate abnormality based at least in part on the abnormality skewness value.

22. The method of claim 21, wherein the likelihood determining step comprises:

comparing the abnormality skewness value to a threshold; and

determining the candidate abnormality to be malignant if the abnormality skewness value exceeds the threshold, and to be benign if the abnormality threshold exceeds the abnormality skewness value.