Method and apparatus for automated detection of masses in digital images

• US 6,580,818 B2
• Filed: 10/09/2001
• Issued: 06/17/2003
• Est. Priority Date: 06/03/1997
• Status: Expired due to Term

• Current Assignee: Hologic Incorporated
• Original Assignee: Altera Corporation (Intel Corporation)
• Inventors: te Brake, Guido M., Karssemeijer, Nico
• Primary Examiner(s): Patel, Jayanti K.
• Application Number:
  US09/974,317
• Publication Number:
  US 20020054700A1
• Time in Patent Office: 616 Days
• Field of Search: 382/100, 382/128-134, 382/155, 382/156, 382/194, 382/195, 382/308, 382/260, 378/98.11, 378/1, 378/62-63, 378/37, 600/408, 736/02
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/64 of convexity or concavity
• CPC Class Codes: G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components

Abstract

A method and apparatus for the automated detection of masses in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing suspicious portions of the digital mammogram. A gradient image is created from the digital mammogram, and information in the gradient image is processed for identifying masses. In a preferred embodiment, a portion of a spiculation detection algorithm is applied to the gradient image for identifying masses. The spiculation detection algorithm comprises a line detection portion and a post-line detection portion, and it is the post-line detection portion which is applied to the gradient image for identifying masses. Advantageously, computer programs which have already been written for spiculation detection may, with minor modifications, be ported into mass detection programs.

Claims

1. A method of detecting masses in a digital image, comprising the steps of:

computing a gradient plane from said digital image;

processing information in said gradient plane for identifying masses in said digital image by applying a portion of a spiculation detection algorithm to said gradient plane,

wherein said spiculation detection algorithm comprises;

a line detection step for generating line information and direction information corresponding to the digital image; and

a post-line detection step for identifying spiculations in the digital image using said line information and said direction information;

wherein the portion of said spiculation algorithm which is applied to said gradient plane is said post-line detection step.

2. The method of claim 1, said gradient plane comprising gradient magnitude information and gradient direction information, wherein:

when said post-line detection step of said spiculation detection algorithm is used in said spiculation detection algorithm, said post-line detection step receives a first input equal to said line information from said line detection step of said spiculation detection algorithm, said post-line detection step of the spiculation detection algorithm also receiving a second input, said second input being equal to said direction information from said line detection step of said spiculation detection algorithm; and

wherein

when said post-line detection step of said spiculation detection algorithm is applied to said gradient plane, said gradient magnitude information is received as said first input and said gradient direction information is received as said second input.

3. The method of claim 2, wherein:

when said post-line detection step of said spiculation detection algorithm is used in said spiculation detection algorithm, said post-line detection step generates a first output corresponding to spiculation location information; and

wherein

when said post-line detection step of said spiculation detection algorithm is applied to said gradient plane, said first output corresponds to mass location information.

4. The method of claim 3, wherein:

when said post-line detection step of said spiculation detection algorithm is used in said spiculation detection algorithm, said post-line detection step generates a second output corresponding to spiculation intensity information; and

wherein

when said post-line detection step of said spiculation detection algorithm is applied to said gradient plane, said first output corresponds to mass intensity information.

5. The method of claim 4, said digital image comprising pixels, said line information and said direction information formed in a line image plane comprising pixels, wherein said post-line detection step of said spiculation detection algorithm comprises the steps of:

receiving said line image plane;

selecting a set of candidate pixels in said digital image;

for each candidate pixel;

selecting a neighborhood of pixels near said candidate pixel;

selecting a small region around said candidate pixel;

computing a first spiculation metric proportional to the number of pixels in said neighborhood which are located along lines in said line image plane and which have direction information pointing toward said small region; and

evaluating said first spiculation metrics of said candidate pixels for determining the locations of spiculations in said digital image.

6. The method of claim 5, said post-line detection step of said spiculation detection algorithm further comprising the steps of:

for each candidate pixel;

computing a second spiculation metric corresponding to the spatial distribution of those pixels in said neighborhood which are located along lines in said line image plane and which have direction information pointing toward said small region, said second spiculation metric increasing according to the isotropy of said spatial distribution around said candidate pixel; and

evaluating said first and second spiculation metrics of said candidate pixels for determining the locations of spiculations in said image.

7. The method of claim 6, wherein said set of candidate of pixels comprises each pixel in said line image.

8. A method of detecting masses in a digital image, comprising the steps of:

computing a gradient plane from said digital image, said gradient plane comprising pixels, each pixel having gradient magnitude and gradient direction information;

selecting a set of candidate pixels in digital image image;

for each candidate pixel, computing a first density metric based on a first set of surrounding pixels having gradient magnitudes above a first threshold and having gradient directions pointing generally toward said candidate pixel; and

evaluating said first density metrics for determining the locations of masses in said digital image.

9. The method of claim 8, further comprising the steps of:

for each candidate pixel, computing a second density metric corresponding to a spatial distribution of said first set of pixels, said second density metric corresponding to the isotropy of said spatial distribution around said candidate pixel; and

evaluating said first and second density metrics of said candidate pixels for determining the locations of masses in said digital image.

10. The method of claim 9, said candidate pixels being identified according to an index icand, said first density metric for the icand^th candidate pixel being denoted G1_icand, said first density metric G1_icand being computed according to the steps of:

selecting a neighborhood of pixels NH_icand around said candidate pixel;

selecting a small region R_icand around said candidate pixel;

selecting said first set of pixels from a set of pixels lying in said neighborhood NH_icand having directions which point toward said small region R_icand; and

counting the number of pixels in said first set;

wherein said first density metric G1_icand is proportional to the number of pixels in said first subset.

11. The method of claim 10, said first set of pixels corresponding to the icand^th candidate pixel being denoted by an index (icand,jpoint), said second density metric for the icand^th candidate pixel being denoted G2_icand, said second density metric G2_icand being computed according to the steps of:

selecting K spatial bins (icand,k) extending radially from said candidate pixel and being arranged in a radially symmetric manner around said candidate pixel;

for each pixel (icand,jpoint) of said first set of pixels, identifying the spatial bin (icand,k) in which said pixel (icand,jpoint) is located; and

computing a number of pixels n_icand,k in each bin (icand,k);

wherein said second density metric G2_icand is based on the statistical distribution of the number n_icand,k as k is varied.

12. The method of claim 11, wherein G2_icand is proportional to the number of values k for which n_icand,k is greater than a median value calculated for random orientations.

13. The method of claim 10, wherein said neighborhood of pixels NH_icand forms an annular region around said icand^th candidate pixel.

14. The method of claim 13, wherein said small region R_icand is a circular region lying withing said annular region formed by said neighborhood of pixels neighborhood of pixels NH_icand.

15. The method of claim 9, wherein said step of evaluating said first and second density metrics is performed according to a linear classifier method.

16. The method of claim 9, wherein said step of evaluating said first and second density metrics is performed according to a neural network method.

17. The method of claim 8, wherein said set of candidate pixels comprises each pixel in said gradient plane.

18. A method of detecting masses in a digital image, comprising the steps of:

computing a gradient plane from said digital image, said gradient plane comprising pixels, each pixel having gradient magnitude and gradient direction information;

selecting a set of candidate pixels in said gradient plane, said candidate pixels being denoted by an index icand;

for each candidate pixel icand, computing a first density metric G1_icand according to the steps of;

selecting a neighborhood of pixels NH_icand around said candidate pixel;

selecting a small region R_icand around said candidate pixel;

selecting a first set of pixels in said neighborhood NH_icand having gradient directions pointing toward said small region R_icand and having a gradient magnitude greater than a predetermined lower threshold, said first set of pixels being denoted by the counter variable jpoint; and

counting the number of pixels in said first set, wherein said first density metric G1_icand is proportional to the number of pixels in said first set;

for each candidate pixel icand, computing a second density metric G2_icand according to the steps of;

selecting K spatial bins (icand,k) extending radially from said candidate pixel and being arranged in a radially symmetric manner around said candidate pixel;

for each pixel (icand,jpoint) of said first set of pixels, identifying the spatial bin (icand,k) in which said pixel (icand,jpoint) is located; and

computing a number of pixels n_icand,k in each spatial bin (icand,k), wherein said second density metric G2_icand is based on the statistical distribution of the number n_icand,k as k is varied; and

evaluating said first and second density metrics G1_icand and G2_icand according to a linear classifier method for determining the locations of masses in said digital image.