METHOD AND SYSTEM FOR DETECTING CANCER REGIONS IN TISSUE IMAGES

• US 20080170766A1
• Filed: 12/31/2007
• Published: 07/17/2008
• Est. Priority Date: 01/12/2007
• Status: Abandoned Application

• Current Assignee: Medical Diagnostic Technologies, Inc. (Bio-Techne Corporation)
• Original Assignee: Medical Diagnostic Technologies, Inc. (Bio-Techne Corporation)
• Inventors: Yfantis, Spyros A.
• Application Number:
  US11/967,560
• Publication Number:
  US 20080170766A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06V 10/431 Frequency domain transformation; Autocorrelation
• CPC Class Codes: G06V 2201/032 of protuberances, polyps nodules, etc.

Abstract

A pixel of an image is classified between a first kind and a second kind by centering a sample mask on the pixel and applying each of a population of R given basis functions to the mask pixels to generate, for each basis function, a bucket of values. A probability density function is estimated for each of the bucket of values. Each of the R probability density functions is transformed to a single valued result, to generate an R-dimensional sample classification vector. The R-dimensional sample classification vector is classified against a R-dimensional first centroid vector and a R-dimensional second centroid vector, each of centroid vectors constructed in a previous training of applying the same population of R given basis functions to pixels known as being the first kind and to pixels known as being the second kind. Optionally, pixels may be conditionally classified and then finally classified based on subsequent classification of neighbor pixels.

Claims

1. A method for classifying pixels of a pixel image representing a substance into at least two different classes, comprising:

providing a plurality of at least R basis functions;

providing a classification criterion;

receiving an external pixel image;

identifying a subject pixel from said received external pixel image

identifying a spatial window of pixels aligned spatially with the subject pixel;

generating a plurality of R buckets of computed values, each of said buckets based on applying a corresponding one of said R basis functions to pixels within the spatial window;

generating a plurality of R histograms, each of said histograms reflecting an estimated probability density function a corresponding one of said R buckets of values;

transforming said plurality of R histograms into an R-dimensional sample classification vector;

generating a classification data representing one of said two different classes for said subject pixel, based on said R-dimensional sample classification vector and said classification criterion.

2. The method of claim 1, wherein said classification criterion includes an R-dimensional classification vector, and wherein generating a classification data includes transforming said generated plurality of R estimated probability densities into an R-dimensional sample vector.

3. The method of claim 2, wherein said classification criterion further includes a second R-dimensional classification vector, and wherein said generating a classification data includes calculating a first distance from said R-dimensional sample vector to said R dimensional classification vector, calculating a second distance from said R-dimensional sample vector to said second R dimensional classification vector, and a comparative magnitude of said first distance and said second distance.

4. The method of claim 3, wherein said first distance is a first Mahalanobis distance and said second distance is a second Mahalanobis distance.

5. The method of claim 1, wherein said providing a classification criterion includes:

providing a plurality of pixel images, each having at least one pixel having a known classification kind;

selecting a subject pixel from said plurality of pixel images having the known classification kind;

selecting a sample mask of pixels from said pixel image, having the subject pixel and a neighborhood of surrounding pixels;

generating a plurality of R buckets of computed values, each of said buckets based on applying a corresponding one of said R basis functions to pixels within the spatial window;

generating a plurality of R histograms, each of said histograms reflecting an estimated probability density function a corresponding one of said R buckets of values;

transforming said plurality of R histograms into an R-dimensional training classification vector;

selecting another subject pixel from said plurality of pixel images, the pixel having the known classification kind;

repeating said selecting mask, selecting a basis function, generating a plurality of R buckets of values, generating a plurality of R histograms, and transforming, until a plurality of at least G training vectors are generated; and

generating a first centroid vector based on an average of said plurality of at least G training vectors.

12. The method of claim 1, wherein said different classes comprise a first class representing a non-cancerous condition and a second class representing a cancerous condition.

13. The method of claim 5, wherein said pixel image represents an image of a tissue and wherein the different classes comprise a first class representing a non-cancerous condition and a second class representing a cancerous condition.

14. The method of claim 5, wherein said pixel image represents an mage of a prostate and wherein the different classes comprise a first class representing a non-cancerous condition and a second class representing a cancerous condition.

6. A method for classifying pixels of a pixel image representing a substance into at least two different classes, comprising providing a plurality of at least R basis functions;

providing a classification criterion;

providing a pixel image;

selecting a subject pixel from the pixel image;

selecting a sample mask of pixels relative to said subject pixel;

selecting a basis function from said at least R basis functions;

generating a bucket of values, by applying said basis function to each of said M pixel pairs to generate a bucket of M values, where said sequence of M pixel pairs is selected based at least on said basis function;

selecting another basis function;

repeating said generating another a bucket of values and said selecting another basis function, to generate another bucket of values, until all of said R basis functions are selected, to generate a plurality of R of said buckets of values;

generating a plurality of R estimated probability densities, each of said densities based on a corresponding one of said R buckets of values; and

generating a classification data for said subject pixel, based on at least one of said generated estimated probability densities and said classification criterion.

15. The method of claim 6, wherein said pixel image represents an image of a prostate and wherein the different classes comprise a first class representing a non-cancerous condition and a second class representing a cancerous condition.

7. A machine-readable storage medium to provide instructions, which if executed on the machine performs operations comprising:

providing a plurality of at least R basis functions;

providing a classification criterion;

receiving an external pixel image;

identifying a subject pixel from said received external pixel image

identifying a spatial window of pixels aligned spatially with the subject pixel;

generating a plurality of R buckets of computed values, each of said buckets based on applying a corresponding one of said R basis functions to pixels within the spatial window;

generating a plurality of R histograms, each of said histograms reflecting an estimated probability density function a corresponding one of said R buckets of values;

transforming said plurality of R histograms into an R-dimensional sample classification vector;

generating a classification data for said subject pixel, based on said R-dimensional sample classification vector and said classification criterion.

8. The machine readable storage medium of of claim 7, further providing instructions, which if executed on the machine performs operations comprising said classification criterion including an R-dimensional classification vector, and wherein generating a classification data includes transforming said generated plurality of R estimated probability densities into an R-dimensional sample vector.

9. The machine readable storage medium of claim 8, further providing instructions, which if executed on the machine performs operations comprising:

said classification criterion further includes a second R-dimensional classification vector, and wherein said generating a classification data includes calculating a first distance from said R-dimensional sample vector to said R dimensional classification vector, calculating a second distance from said R-dimensional sample vector to said second R dimensional classification vector, and a comparative magnitude of said first distance and said second distance.

10. The machine readable storage medium of claim 8, further providing instructions, which if executed on the machine performs operations comprising:

said first distance is a first Mahalanobis distance and said second distance is a second Mahalanobis distance.

11. An ultrasound image recognition system comprising:

an ultrasound scanner having an RF echo output, an analog to digital (A/D) frame sampler for receiving the RF echo output, a machine arranged for executing machine-readable instructions, and a machine-readable storage medium to provide instructions, which if executed on the machine, perform operations comprising;

providing a plurality of at least R basis functions;

providing a classification criterion;

receiving an external pixel image;

identifying a subject pixel from said received external pixel image

identifying a spatial window of pixels aligned spatially with the subject pixel;

generating a plurality of R buckets of computed values, each of said buckets based on applying a corresponding one of said R basis functions to pixels within the spatial window;

generating a plurality of R histograms, each of said histograms reflecting an estimated probability density function a corresponding one of said R buckets of values;

transforming said plurality of R histograms into an R-dimensional sample classification vector;

generating a classification data for said subject pixel, based on said R-dimensional sample classification vector and said classification criterion.

16. The method of claim 11, wherein said pixel image represents an image of a prostate and wherein the different classes comprise a first class representing a non-cancerous condition and a second class representing a cancerous condition.