Method and apparatus for diagnosis of breast tumors

Claim 1

A method for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: digitizing selected ultrasonic images from a plurality of patients generating an array of pixel intensity values; entering into a computer said digitized ultrasonic images and target data based on excisional biopsy results for each of said patients where the candidate tissue is identified as malignant or benign; locating and defining a region of interest in said digitized ultrasonic images, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; generating a first feature value from said arrays of pixels corresponding to an angular second moment of said pixel intensity values; generating a second feature value from said arrays of pixels corresponding to an inverse contrast of said pixel intensity values; generating a third feature value from said arrays of pixels corresponding to a short run emphasis of said pixel intensity values; applying a set of weights to said first, second, and third feature values for generating values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign; calculating a deviation between said output values with said target data; and altering said weights until said deviation is reduced to a predetermined value.

Claim 2

A method for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: digitizing selected ultrasonic images from a plurality of patients generating an array of pixel intensity values; entering into a computer said digitized ultrasonic images and target data based on excisional biopsy results for each of said patients where the candidate tissue is identified as malignant or benign; locating and defining a region of interest in said digitized ultrasonic images, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; generating a first feature value from said arrays of pixels corresponding to an angular second moment of said pixel intensity values; generating a second feature value from said arrays of pixels corresponding to an inverse contrast of said pixel intensity values; generating a third feature value from said arrays of pixels corresponding to a short run emphasis of said pixel intensity values; applying a set of weights to said first, second, and third feature values for generating values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign; calculating a deviation between said output values with said target data; and altering said weights until said deviation is reduced to a predetermined value.

Claim 4

A method for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: digitizing said ultrasonic image generating an array of pixel intensity values; locating and defining a region of interest in said digitized ultrasonic image, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; generating a first feature value from said array of pixels corresponding to a angular second moment of said pixel intensity values; generating a second feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; generating a third feature value from said array of pixels corresponding to a short run emphasis of said pixel intensity values; and applying a set of weights to said first, second and third feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign.

Claim 5

A method for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: digitizing said ultrasonic image generating an array of pixel intensity values; locating and defining a region of interest in said digitized ultrasonic image, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; generating a first feature value from said array of pixels corresponding to a angular second moment of said pixel intensity values; generating a second feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; generating a third feature value from said array of pixels corresponding to a short run emphasis of said pixel intensity values; and applying a set of weights to said first, second and third feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign.

Claim 6

An apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: means for digitizing selected ultrasonic images from a plurality of patients generating an array of pixel intensity values; means for entering into a computer said digitized ultrasonic images and target data based on excisional biopsy results for each of said patients where the candidate tissue is identified as malignant or benign; means for locating and defining a region of interest in said digitized ultrasonic images, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; first extraction means for generating a first feature value from said arrays of pixels corresponding to a angular second moment of said pixel intensity values; second extraction means for generating a second feature value from said arrays of pixels corresponding to a inverse contrast of said pixel intensity values; third extraction means for generating a third feature value from said arrays of pixels corresponding to a short run emphasis of said pixel intensity values; an untrained neural network means coupled to said first, second and third feature value extraction means for determining a set of weights to said feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign; means for calculating a deviation between said output values with said target data; and means for altering said weights until said deviation is reduced to a predetermined value.

Claim 7

An apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: means for digitizing selected ultrasonic images from a plurality of patients generating an array of pixel intensity values; means for entering into a computer said digitized ultrasonic images and target data based on excisional biopsy results for each of said patients where the candidate tissue is identified as malignant or benign; means for locating and defining a region of interest in said digitized ultrasonic images, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; first extraction means for generating a first feature value from said arrays of pixels corresponding to a angular second moment of said pixel intensity values; second extraction means for generating a second feature value from said arrays of pixels corresponding to a inverse contrast of said pixel intensity values; third extraction means for generating a third feature value from said arrays of pixels corresponding to a short run emphasis of said pixel intensity values; an untrained neural network means coupled to said first, second and third feature value extraction means for determining a set of weights to said feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign; means for calculating a deviation between said output values with said target data; and means for altering said weights until said deviation is reduced to a predetermined value.

Claim 9

An apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: means for digitizing said ultrasonic image generating an array of pixel intensity values; means for locating and defining a region of interest in said digitized ultrasonic image, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; first extraction means for generating a first feature value from said array of pixels corresponding to a angular second moment of said pixel intensity values; second extraction means for generating a second feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; third extraction means for generating a third feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; a trained neural network means coupled to said first, second and third feature network means coupled to said first, second and third feature value extraction means for applying a set of weights to said feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign.

Claim 10

An apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient, comprising: means for digitizing said ultrasonic image generating an array of pixel intensity values; means for locating and defining a region of interest in said digitized ultrasonic image, which includes substantially all of the candidate tissue and excludes substantially all normal tissue; first extraction means for generating a first feature value from said array of pixels corresponding to a angular second moment of said pixel intensity values; second extraction means for generating a second feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; third extraction means for generating a third feature value from said array of pixels corresponding to a inverse contrast of said pixel intensity values; a trained neural network means coupled to said first, second and third feature network means coupled to said first, second and third feature value extraction means for applying a set of weights to said feature values for generating network output values between an upper and lower value, whereby said output values tend toward said upper value when the candidate tissue is malignant and said output values tend toward said lower value when the candidate tissue is benign.