METHOD AND SYSTEM FOR GLEASON SCALE PATTERN RECOGNITION

• US 20080170767A1
• 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:
  US11/967,644
• Publication Number:
  US 20080170767A1
• 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 gradient is calculated for a pixel image representing a cancer area, the gradient having magnitude and direction. Local extrema of the gradients are identified, remaining pixels are zeroed. Significant gradients among the local extrema are identified, based on thresholds obtained through training. Probability distributions are calculated for the local extrema magnitudes and the significant gradient magnitudes, to form a feature vector. The feature vector is classified using a Maximum Likelihood Estimate classifier constructed from large training sets.

Claims

1. A method for classifying pixels of a pixel image representing a substance into one of multiple classes, comprising:

providing a maximum likelihood estimation model for each of a plurality of cancer conditions, each model having a first gradient probability distribution parameter, a cancerous region area parameter, a significant gradient magnitude distribution parameter, and a gradient phase distribution;

providing a pixel array representing a tissue having a cancer;

generating an array of gradient values, each value having a gradient magnitude and a gradient angle, each value corresponding to a pixel array;

generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

generating a first gradient probability distribution function, based on a cancerous tissue, and based on said teacher basis;

generating an array of significant gradients, based on the array of gradient values and on a given low threshold and a given high threshold;

generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients;

performing a maximum likelihood estimate, based on said first gradient, said probability distribution function, said first gradient probability distribution said cancerous tissue area value, and said phase distribution, to maximize the probability of the subject pixel being in one, and not all, classification.

2. The method of claim 1, further comprising generating a significant gradient percentage data based on a comparative population of said significant gradients to the population of said pixels, wherein said performing said maximum likelihood estimate is further based on said generated significant gradient percentage data.

3. The method of claim 1, wherein the provided maximum likelihood estimation model includes prostate cancer conditions, said conditions including a plurality of Gleason scores, and wherein said performing a maximum likelihood estimate generates an estimate of the Gleason score of the tissue corresponding to the pixel being classified.

4. The method of claim 1, wherein said providing a maximum likelihood estimation model for each of a plurality of cancer conditions includes providing a large sample set of images having known cancerous regions having a known first Gleason scale score:

i) selecting an image area of Q×

R pixels from one of said images, the selected image area having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

ii) generating an array of gradient values for each pixel in the Q×

R array, each value having a gradient magnitude and a gradient angle;

iii) generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

iv) generating a first gradient probability distribution function based on the array of local maxima gradients;

v) initializing an upper cut-off threshold T_U and a lower cutoff threshold T_L;

vi) generating an array of significant gradients, based on the array of gradient values and on said upper cut-off threshold T_U and lower cutoff threshold T_L;

vii) generating a significant gradient quantity data, based on a comparative population of said significant gradients to the population of said pixels in said P×

Q array;

viii) generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

ix) generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients; and

x) generating a first training vector based on said first gradient probability distribution, said significant gradient probability distribution function, said phase distribution function, and said significant gradient quantity data;

xi) selecting another image area of P×

Q pixels having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xii) repeating (ii) through (xi) a predetermined number of time to generate a predetermined quantity of first training vectors; and

xiii) averaging the quantity of first training vectors to generate a first centroid, wherein said first centroid defines a first of said classes.

5. The method of claim 4, wherein said providing a maximum likelihood estimation model for each of a plurality of cancer conditions includes providing a large sample set of images having known cancerous regions having a known second Gleason scale score:

xiv) selecting an image area of Q×

R pixels from one of said images, the selected image area having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xv) generating an array of gradient values for each pixel in the Q×

R array, each value having a gradient magnitude and a gradient angle;

xvi) generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

xvii) generating a first gradient probability distribution function based on the array of local maxima gradients;

xviii) initializing an upper cut-off threshold T_U and a lower cutoff threshold T_L;

xix) generating an array of significant gradients, based on the array of gradient values and on said upper cut-off threshold T_U and lower cutoff threshold T_L;

xx) generating a significant gradient quantity data, based on a comparative population of said significant gradients to the population of said pixels in said P×

Q array;

xxi) generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

xxii) generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients; and

xxiii) generating a first training vector based on said first gradient probability distribution, said significant gradient probability distribution function, said phase distribution function, and said significant gradient quantity data;

xxiv) selecting another image area of P×

Q pixels having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xxv) repeating (xv) through (xvi) a predetermined number of time to generate a predetermined quantity of second training vectors; and

xxvi) averaging the quantity of second training vectors to generate a second centroid, wherein said second centroid defines a second of said classes.

6. The method of claim 5, further comprising an optimization of the cut-off threshold T_U and a lower cutoff threshold T_L image area, comprising:

a testing, comprising;

inputting a plurality of first test image areas of Q×

R pixels, each having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition,

inputting a plurality of second test image areas of Q×

R pixels, each having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition,

classifying the first test images and the second test images against the maximum likelihood model having said first centroid and said second centroid to generate an error measure;

changing at least one of said cut-off threshold T_U and a lower cutoff threshold T_L image area and repeating (i) though (xxvi) to generate another first centroid and another second centroid;

repeating said testing to generate another test measure; and

repeating said changing and said repeating said testing until a given optimum error is identified.

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

providing a maximum likelihood estimation model for each of a plurality of cancer conditions, each model having a first gradient probability distribution parameter, a cancerous region area parameter, a significant gradient magnitude distribution parameter, and a gradient phase distribution;

providing a pixel array representing a tissue having a cancer;

generating an array of gradient values, each value having a gradient magnitude and a gradient angle, each value corresponding to a pixel array;

generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

generating a first gradient probability distribution function, based on a cancerous tissue, and based on said teacher basis;

generating an array of significant gradients, based on the array of gradient values and on a given low threshold and a given high threshold;

generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients;

performing a maximum likelihood estimate, based on said first gradient, said probability distribution function, said first gradient probability distribution said cancerous tissue area value, and said phase distribution, to maximize the probability of the subject pixel being in one, and not all, classification.

8. The machine-readable storage medium of claim 7, to provide instructions, which if executed on the machine, further performs operations comprising:

generating a significant gradient percentage data based on a comparative population of said significant gradients to the population of said pixels, wherein said performing said maximum likelihood estimate is further based on said generated significant gradient percentage data.

9. The machine-readable storage medium of claim 7, to provide instructions, which if executed on the machine, further performs operations comprising providing the maximum likelihood estimation model to include prostate cancer conditions, said conditions including a plurality of Gleason scores, and wherein said performing a maximum likelihood estimate generates an estimate of the Gleason score of the tissue corresponding to the pixel being classified.

10. The machine-readable storage medium of claim 7, to provide instructions, which if executed on the machine, further performs operations comprising:

i) selecting an image area of Q×

R pixels from one of said images, the selected image area having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

ii) generating an array of gradient values for each pixel in the Q×

R array, each value having a gradient magnitude and a gradient angle;

iii) generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

iv) generating a first gradient probability distribution function based on the array of local maxima gradients;

v) initializing an upper cut-off threshold T_U and a lower cutoff threshold T_L;

vi) generating an array of significant gradients, based on the array of gradient values and on said upper cut-off threshold T_U and lower cutoff threshold T_L;

vii) generating a significant gradient quantity data, based on a comparative population of said significant gradients to the population of said pixels in said P×

Q array;

viii) generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

ix) generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients; and

x) generating a first training vector based on said first gradient probability distribution, said significant gradient probability distribution function, said phase distribution function, and said significant gradient quantity data;

xi) selecting another image area of P×

Q pixels having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xii) repeating (ii) through (xi) a predetermined number of time to generate a predetermined quantity of first training vectors; and

xiii) averaging the quantity of first training vectors to generate a first centroid, wherein said first centroid defines a first of said classes.

11. The machine-readable storage medium of claim 10, to provide instructions, which if executed on the machine, further performs operations comprising:

xiv) selecting an image area of Q×

R pixels from one of said images, the selected image area having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xv) generating an array of gradient values for each pixel in the Q×

R array, each value having a gradient magnitude and a gradient angle;

xvi) generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

xvii) generating a first gradient probability distribution function based on the array of local maxima gradients;

xviii) initializing an upper cut-off threshold T_U and a lower cutoff threshold T_L;

xix) generating an array of significant gradients, based on the array of gradient values and on said upper cut-off threshold T_U and lower cutoff threshold T_L;

xx) generating a significant gradient quantity data, based on a comparative population of said significant gradients to the population of said pixels in said P×

Q array;

xxi) generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

xxii) generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients; and

xxiii) generating a first training vector based on said first gradient probability distribution, said significant gradient probability distribution function, said phase distribution function, and said significant gradient quantity data;

xxiv) selecting another image area of P×

Q pixels having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xxv) repeating (xv) through (xvi) a predetermined number of time to generate a predetermined quantity of second training vectors; and

xxvi) averaging the quantity of second training vectors to generate a second centroid, wherein said second centroid defines a second of said classes.

12. The machine-readable storage medium of claim 11, to provide instructions, which if executed on the machine, further performs operations comprising:

a testing, comprising;

inputting a plurality of first test image areas of Q×

R pixels, each having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition,

inputting a plurality of second test image areas of Q×

R pixels, each having a known value of the area of tissue having the known second Gleason scale score, and a known value of the area of tissue having a non-cancerous condition,

classifying the first test images and the second test images against the maximum likelihood model having said first centroid and said second centroid to generate an error measure;

changing at least one of said cut-off threshold T_U and a lower cutoff threshold T_L image area and repeating (i) though (xxvi) to generate another first centroid and another second centroid;

repeating said testing to generate another test measure; and

repeating said changing and said repeating said testing until a given optimum error is identified.

13. 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 maximum likelihood estimation model for each of a plurality of cancer conditions, each model having a first gradient probability distribution parameter, a cancerous region area parameter, a significant gradient magnitude distribution parameter, and a gradient phase distribution;

providing a pixel array representing a tissue having a cancer;

generating an array of gradient values, each value having a gradient magnitude and a gradient angle, each value corresponding to a pixel array;

generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

generating a first gradient probability distribution function, based on a cancerous tissue, and based on said teacher basis;

generating an array of significant gradients, based on the array of gradient values and on a given low threshold and a given high threshold;

generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients;

performing a maximum likelihood estimate, based on said first gradient, said probability distribution function, said first gradient probability distribution said cancerous tissue area value, and said phase distribution, to maximize the probability of the subject pixel being in one, and not all, classification.

14. The system of claim 13, wherein the machine readable storage medium provides instructions, which if executed on the machine, further performs operations comprising:

i) selecting an image area of Q×

R pixels from one of said images, the selected image area having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

ii) generating an array of gradient values for each pixel in the Q×

R array, each value having a gradient magnitude and a gradient angle;

iii) generating an array of local maxima gradients, each corresponding to one value of the array of gradient values, and each indicating a magnitude of a pixel of the pixel array having a relative magnitude larger than a neighbor pixel;

iv) generating a first gradient probability distribution function based on the array of local maxima gradients;

v) initializing an upper cut-off threshold T_U and a lower cutoff threshold T_L;

vi) generating an array of significant gradients, based on the array of gradient values and on said upper cut-off threshold T_U and lower cutoff threshold T_L;

vii) generating a significant gradient quantity data, based on a comparative population of said significant gradients to the population of said pixels in said P×

Q array;

viii) generating a phase distribution, representing a probability distribution of the gradient angle of said array of significant gradients;

ix) generating a significant gradient probability distribution function characterizing a probability distribution of the magnitude of the significant gradients; and

x) generating a first training vector based on said first gradient probability distribution, said significant gradient probability distribution function, said phase distribution function, and said significant gradient quantity data;

xi) selecting another image area of P×

Q pixels having a known value of the area of tissue having the known first Gleason scale score, and a known value of the area of tissue having a non-cancerous condition;

xii) repeating (ii) through (xi) a predetermined number of time to generate a predetermined quantity of first training vectors; and

xiii) averaging the quantity of first training vectors to generate a first centroid, wherein said first centroid defines a first of said classes.