Method and system for analysis of false positives produced by an automated scheme for the detection of lung nodules in digital chest radiographs

• US 5,289,374 A
• Filed: 02/28/1992
• Issued: 02/22/1994
• Est. Priority Date: 02/28/1992
• Status: Expired due to Fees

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Matsumoto, Tsuneo, Kano, Akiko, Giger, Maryellen L., Doi, Kunio
• Primary Examiner(s): Envall, Jr., Roy N.
• Assistant Examiner(s): Thomas, Joseph
• Application Number:
  US07/843,715
• Time in Patent Office: 725 Days
• Field of Search: 364/413.13, 364/413.22, 364/413.23, 364/413.01, 382/6
• US Class Current: 600/407
• CPC Class Codes: G06T 2207/30064 Lung nodule
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/273 removing elements interfering with the pattern to be recognised
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A computerized method and system for reducing the number of false-positive detections of nodule candidates in the detection of abnormalities in digital chest radiography. The image is initially subjected to an image difference technique where the detection sensitivity is increased so as to avoid missing small nodules which might otherwise go undetected. Such a technique tends to increase the number of false-positives, however, leading to possible incorrect diagnoses of the radiographs. To reduce the number of false-positives, feature extraction techniques are applied to grown regions around the nodule candidates, in order to provide computer generated information concerning the candidates. A data base of parameters common to false-positives is compared to calculated parameters of a candidate of interest. The candidates with grown region parameters within the data base range common to false-positives are eliminated as being probable false-positive detections due to normal background anatomical features.

Claims

1. A method for reducing the number of false-positive detections in a computerized scheme for detecting true-positive abnormalities on chest radiographs, comprising the steps of:

(a) storing a plurality of predetermined threshold values in memory, wherein said plurality of predetermined threshold values are characteristics of known false-positives related to average edge-gradient, edge-gradient distribution, standard deviation of edge-gradient orientation, transition point information, size, circularity, shape irregularity and contrast values;

(b) generating a digital image of an object;

(c) subjecting said digital image to a signal-to-noise ratio (SNR) enhancement filtering process and an SNR suppressed filtering process in order to produce respective first and second filtered digital images;

(d) obtaining a difference image by subtracting said second filtered digital image from said first filtered digital image, thereby enhancing the conspicuity of abnormality candidates included on said difference image;

(e) detecting the locations of said abnormality candidates included on said difference image;

(f) calculating a first set of parameters related to size, circularity, shape irregularity, edge-gradient, and contrast values for each abnormality candidate detected on said difference image;

(g) eliminating abnormality candidates having said first set of parameters related to size, circularity, shape irregularity, edge-gradient and contrast values within predetermined value ranges corresponding to a first set of said plurality of predetermined threshold values stored in memory;

(h) performing background trend correction on said digital difference image for the abnormality candidates remaining after elimination of said abnormality candidates in step (g);

(i) calculating, for each remaining abnormality candidate, a second set of parameters related to size, circularity, shape irregularity, edge-gradient and transition point; and

(j) eliminating, as false-positives, said remaining abnormality candidates which have at least one of said parameter values calculated in step (i) within a predetermined value range corresponding to a second set of said plurality of predetermined threshold values stored in memory, thus providing an indication of the locations of said true-positive abnormalities on said chest radiographs.

2. The method for reducing the number of false-positive detections in a computerized scheme according to claim 1, wherein step (j) includes eliminating, as false-positives, said remaining abnormality candidates which have an average edge-gradient parameter above a first predetermined threshold, a standard deviation of edge-gradient orientation parameter less than a second predetermined threshold value, a circularity value less than a third predetermined threshold value and a shape irregularity value greater than a fourth predetermined threshold value.

3. The method for reducing the number of false-positive detections in a computerized scheme according to claim 1, wherein step (j) includes eliminating, as false-positives, remaining abnormality candidates with a standard deviation of edge-gradient orientation parameter greater than a fifth predetermined threshold value and having two narrow peaks at a 180°

separation on said edge-gradient orientation distribution.

4. The method for reducing the number of false-positive detections in a computerized scheme according to claim 1, wherein step (j) includes eliminating, as false-positives, remaining abnormality candidates with a contrast value less than a sixth predetermined threshold value, a size greater than a seventh predetermined threshold value, or a contrast value greater than an eighth predetermined threshold value with a size less than a ninth predetermined threshold value.

5. The method for reducing the number of false-positive detections in a computerized scheme according to claim 1, wherein step (j) includes eliminating, as false-positives, remaining abnormality candidates for which a transition point is not found.

6. The method for reducing the number of false-positive detections in a computerized scheme according to claim 1, wherein step (j) includes eliminating, as false-positives, remaining abnormality candidates with a circularity value less than a tenth predetermined threshold value or a shape irregularity less than an eleventh predetermined threshold value.

7. A system for reducing the number of false-positive detections in a computerized scheme for detecting true-positive abnormalities on chest radiographs, comprising:

means for storing a plurality of predetermined threshold values, wherein said threshold values are characteristics of known false-positives related to average edge-gradient, edge-gradient distribution, standard deviation of edge-gradient orientation, transition point information, size, circularity, shape irregularity and contrast values;

means for generating a digital image of an object;

means for subjecting said digital image to a signal-to-noise ratio (SNR) enhancement filtering process and a signal-to-noise (SNR) suppressed filtering process in order to produce respective first and second filtered digital images;

means for obtaining a difference image by subtracting said second filtered digital image from said first filtered digital image, thereby enhancing the conspicuity of abnormality candidates included on said difference image;

means for calculating a first set of parameters related to size, circularity, shape irregularity, edge-gradient and contrast values for each abnormality candidate;

means for eliminating abnormality candidates having said first set of parameters related to size, circularity, shape irregularity, edge-gradient and contrast values within predetermined value ranges corresponding to a first set of said plurality of predetermined threshold values stored in memory;

means for performing background trend correction on said digital difference image for the abnormality candidates remaining after elimination of said abnormality candidates having size, circularity, shape irregularity, edge-gradient and contrast parameters within said predetermined value ranges;

means for calculating, for each remaining abnormality candidate, a second set of parameters related to size, circularity, shape irregularity, edge-gradient and transition point; and

means for eliminating, as false-positives, said remaining abnormality candidates which have at least one of said second set of parameters within a predetermined value range corresponding to a second set of said plurality of predetermined threshold values stored in memory, thus providing an indication of the locations of said true-positive abnormalities on said chest radiographs.

8. The system according to claim 7, wherein said means for eliminating, as false-positives, said remaining abnormality candidates includes means for eliminating those abnormality candidates having an average edge-gradient value above a first predetermined threshold, a standard deviation of edge-gradient orientation less than a second predetermined threshold value, a circularity less than a third predetermined threshold value and a shape irregularity value greater than a fourth predetermined threshold value.

9. The system according to claim 7, wherein said means for eliminating, as false-positives, said remaining abnormality candidates includes means for eliminating abnormality candidates having a standard deviation of edge-gradient orientation parameter greater than a fifth predetermined threshold value and two narrow peaks at a 180°

separation of said edge-gradient orientation distribution.

10. The system according to claim 7, wherein said means for eliminating, as false-positives, said remaining abnormality candidates includes means for eliminating abnormality candidates having a contrast value less than a sixth predetermined threshold value with a size greater than a seventh predetermined threshold value, or a contrast value greater than an eighth predetermined threshold value with a size less than a ninth predetermined threshold value.

11. The system according to claim 7, wherein said means for eliminating, as false-positives, said remaining abnormality candidates includes mans for eliminating those candidates for which no transition point is found.

12. The system according to claim 7, wherein said means for eliminating, as false-positives, said remaining abnormality candidates includes means for eliminating abnormality candidates having a circularity less than a tenth predetermined threshold value or a shape irregularity less than an eleventh predetermined threshold value.