US6246782B1 — System for automated detection of cancerous masse…

Claim 1

Independent Claim 139 words

A method for automated analysis of a digitized mammogram to detect the presence of a possible cancerous mass comprising: detecting a region of interest (ROI) using said digitized mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digitized mammogram are employed to identify at least one brightness peak corresponding with the ROI; extracting context data for said ROI from said digitized mammogram, said context data comprising attribute information determined in specific relation to the ROI; and inputting image data corresponding with said ROI and said context data to a neural net trained in relation to the attributes of cancerous tissue regions, and generating an output from said neural net indicating whether a possible cancerous tissue mass is present in said ROI.

Claim 2

Independent Claim 139 words

A method for automated analysis of a digitized mammogram to detect the presence of a possible cancerous mass comprising: detecting a region of interest (ROI) using said digitized mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digitized mammogram are employed to identify at least one brightness peak corresponding with the ROI; extracting context data for said ROI from said digitized mammogram, said context data comprising attribute information determined in specific relation to the ROI; and inputting image data corresponding with said ROI and said context data to a neural net trained in relation to the attributes of cancerous tissue regions, and generating an output from said neural net indicating whether a possible cancerous tissue mass is present in said ROI.

Claim 3

Dependent Claim 23 words

The method of claim 1 wherein the step of detecting a ROI is performed by an optical processor separate from said neural net.

Claim 4

Dependent Claim 31 words

The method of claim 1 further comprising the step of averaging pixels corresponding with only said ROI to create a smaller number of super-pixels that are input to said neural net.

Claim 5

Dependent Claim 14 words

The method of claim 3 wherein said super-pixels are arranged in a radial-polar pattern.

Claim 6

Dependent Claim 58 words

The method of claim 4 , wherein said radial-polar pattern comprises a first predetermined number of equally spaced radial bins and a second predetermined number of equally spaced angular bins, said radial and angular bins defining a plurality of grid spaces, and wherein for each grid space the included ROI pixels are averaged to obtain said super pixels.

Claim 7

Dependent Claim 25 words

The method of claim 1 wherein said context data comprises the location of said ROI relative to a standard anatomical landmark in said digitized mammogram.

Claim 8

Dependent Claim 15 words

The method of claim 1 wherein said context data comprises the size of said ROI.

Claim 9

Dependent Claim 17 words

The method of claim 1 wherein said context data comprises brightness and density profiles of said ROI.

Claim 10

Dependent Claim 23 words

The method of claim 1 wherein said context data comprises the relative isolation of said ROI from other ROIs in said digitized mammogram.

Claim 11

Dependent Claim 20 words

The method of claim 1 wherein said context data comprises the number and length of any spicules in said ROI.

Claim 12

Dependent Claim 18 words

The method of claim 1 wherein said context data comprises information about other ROIs in said digitized mammogram.

Claim 13

Dependent Claim 28 words

The method of claim 1 further comprising the step of enhancing the contours of said ROI using spatial bandpass and spatial wavelet filters before extracting said context data.

Claim 14

Dependent Claim 66 words

The method of claim 1 wherein said neural net comprises: a first neural net receiving image data corresponding with said ROI as an input and generating at least one output value; and a second neural net receiving said output values from said first neural net and said context data as inputs and generating an output indicating whether a possible cancerous mass is present in said ROI.

Claim 15

Dependent Claim 44 words

The method of claim 13 , further comprising the substep of averaging pixels in said digitized mammogram corresponding with said ROI to obtain the image data, and wherein said input to said first neural net includes information corresponding with a size of said ROI.

Claim 16

Dependent Claim 51 words

The method of claim 14 , wherein said context data comprises a least two types of information selected from a group consisting of: ROI location information, ROI size-related information, ROI brightness and density profile information, ROI isolation information relative to other detected regions of interest, ROI information relating to spicules therewithin.

Claim 17

Dependent Claim 25 words

The method of claim 15 , wherein said context data is calculated from sine and cosine transformation of said at least two types of information.

Claim 18

Dependent Claim 43 words

The method of claim 1 wherein said neural net is initially trained using perturbations of a set of training images, said perturbations being generated by at least one of random scaling, rotation and translation of training images comprising said set of training images.

Claim 19

Dependent Claim 24 words

The method of claim 1 , further comprising the step of reducing a resolution of said digitized mammogram for use in the detecting step.

Claim 20

Dependent Claim 38 words

The method of claim 1 , further comprising the step of spatial bandpass filtering said digitized mammogram image to obtain said plurality of spatially bandpassed images, wherein a corresponding plurality of bandpass filters having differing resolutions are employed.

Claim 21

Dependent Claim 106 words

The method of claim 1 , wherein said detecting step includes the substep of estimating, for at least one of said plurality of differing bandpassed images, the size of said at least one brightness peak based upon a determination of the number of pixels around said at least one brightness peak having a brightness within each of the following: a first predetermined percentage of a brightness of the peak in at least one bandpassed image; a second predetermined percentage of a brightness of the digitized mammogram measured at the corresponding peak location; and a third predetermined brightness percentage of a maximum brightness of the digitized mammogram.

Claim 22

Dependent Claim 16 words

The method of claim 20 , wherein a resolution of a said digitized mammogram is reduced.

Claim 23

Dependent Claim 88 words

The method of claim 20 , wherein said estimating substep comprises the additional substep of calculating a radial size of said at least one brightness peak by measuring a distance from said peak in a plurality of directions until corresponding pixel brightness is one of the following: less than 1/exp(2) of the corresponding peak brightness; less than a predetermined percentage of a brightness of the digitized mammogram measured at the corresponding peak location; or greater than a predetermined percentage higher than the next pixel closer to the peak.

Claim 24

Dependent Claim 53 words

The method of claim 1 , wherein said detecting step includes the substep of estimating, for at least one of the said plurality of differing bandpassed images, a size of said at least one brightness peak by comparing said at least one of said plurality of differing bandpassed images with said digitized mammogram.

Claim 25

Dependent Claim 30 words

The method of claim 1 , further comprising the step of applying a predetermined plurality of spatial bandpass and spatial wavelet filters to obtain an enhanced image of said ROI.

Claim 26

Dependent Claim 22 words

The method of claim 24 , wherein said enhanced image is employed to obtain at least a portion of said context data.

Claim 27

Independent Claim 138 words

A system for automated detection of cancerous masses in mammograms comprising: means for inputting a digital mammogram; an optical processor for detecting a region of interest (ROI) using said digital mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digital mammogram are employed to identify at least one brightness peak corresponding with the ROI; means for extracting context data for said ROI from said mammogram, said context data comprising attribute information determined in specific relation to the ROI; and a neural net trained in relation to the attributes of cancerous tissue regions, for receiving image data corresponding with said ROI and said context data as inputs and for generating an output indicating whether said ROI contains a possible cancerous mass.

Claim 28

Independent Claim 138 words

A system for automated detection of cancerous masses in mammograms comprising: means for inputting a digital mammogram; an optical processor for detecting a region of interest (ROI) using said digital mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digital mammogram are employed to identify at least one brightness peak corresponding with the ROI; means for extracting context data for said ROI from said mammogram, said context data comprising attribute information determined in specific relation to the ROI; and a neural net trained in relation to the attributes of cancerous tissue regions, for receiving image data corresponding with said ROI and said context data as inputs and for generating an output indicating whether said ROI contains a possible cancerous mass.

Claim 29

Dependent Claim 30 words

The system of claim 26 further comprising means for averaging pixels corresponding with only said ROI to create a smaller number of super-pixels that are input to said neural net.

Claim 30

Dependent Claim 14 words

The system of claim 27 wherein said super-pixels are arranged in a radial-polar pattern.

Claim 31

Dependent Claim 22 words

The system of claim 26 wherein said context data comprises the location of said ROI relative to the nipple in said mammogram.

Claim 32

Dependent Claim 15 words

The system of claim 26 wherein said context data comprises the size of said ROI.

Claim 33

Dependent Claim 18 words

The system of claim 26 wherein said context data comprises the brightness and density profiles of said ROI.

Claim 34

Dependent Claim 22 words

The system of claim 26 wherein said context data comprises the relative isolation of said ROI from other ROIs in said mammogram.

Claim 35

Dependent Claim 20 words

The system of claim 26 wherein said context data comprises the number and length of any spicules in said ROI.

Claim 36

Dependent Claim 17 words

The system of claim 26 wherein said context data comprises information about other ROIs in said mammogram.

Claim 37

Dependent Claim 31 words

The system of claim 26 wherein said mammogram includes a plurality of views of the same breast, and wherein said context data comprises information from another view of the same breast.

Claim 38

Dependent Claim 24 words

The system of claim 26 wherein said mammogram includes views of both breasts, and wherein said context data comprises information about the other breast.

Claim 39

Dependent Claim 19 words

The system of claim 26 wherein said context data includes information from a previous mammogram for the same patient.

Claim 40

Dependent Claim 21 words

The system of claim 26 further comprising filter means for enhancing the contours of said ROI before extracting said context data.

Claim 41

Dependent Claim 59 words

The system of claim 26 wherein said neural net comprises: a first neural net receiving said ROI as an input and generating at least one output value; and a second neural net receiving said output values from said first neural net and said context data as inputs and generating an output indicating whether said ROI contains a cancerous mass.

Claim 42

Dependent Claim 42 words

The system of claim 26 wherein neural net is initially trained using perturbations of a set of training images, said perturbations being generated by at least one of random scaling, rotation and translation of training images comprising said set of training images.

Claim 43

Independent Claim 182 words

A system for automated detection of cancerous masses in mammograms comprising: means for inputting a digital mammogram containing a first array of pixels; an optical processor to detect a region of interest (ROI) using said digital mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digital mammogram are employed to identify at least one brightness peak corresponding with the ROI; means for averaging pixels of said first array that correspond with only said ROI to create a second array of super-pixels; a first neural net, trained in relation to the attributes of cancerous tissue regions, for receiving said second array of super-pixels as an input and generating at least one output value; and a second neural net, trained in relation to the attributes of cancerous tissue regions, for receiving said at least one output value from said first neural net and at least a portion of said context data as inputs and generating an output indicating whether said ROI contains a possible cancerous mass.

Claim 44

Independent Claim 182 words

A system for automated detection of cancerous masses in mammograms comprising: means for inputting a digital mammogram containing a first array of pixels; an optical processor to detect a region of interest (ROI) using said digital mammogram and Fourier spatial bandpass analysis, said ROI corresponding with a possible cancerous mass, wherein a plurality of spatially bandpassed images of different resolutions corresponding with said digital mammogram are employed to identify at least one brightness peak corresponding with the ROI; means for averaging pixels of said first array that correspond with only said ROI to create a second array of super-pixels; a first neural net, trained in relation to the attributes of cancerous tissue regions, for receiving said second array of super-pixels as an input and generating at least one output value; and a second neural net, trained in relation to the attributes of cancerous tissue regions, for receiving said at least one output value from said first neural net and at least a portion of said context data as inputs and generating an output indicating whether said ROI contains a possible cancerous mass.

Claim 45

Dependent Claim 14 words

The system of claim 41 wherein said super-pixels are arranged in a radial-polar pattern.

Claim 46

Dependent Claim 17 words

The system of claim 41 wherein said context data comprises information about other ROIs in said mammogram.

Claim 47

Dependent Claim 31 words

The system of claim 41 wherein said mammogram includes a plurality of views of the same breast, and wherein said context data comprises information from another view of the same breast.

Claim 48

Dependent Claim 24 words

The system of claim 41 wherein said mammogram includes views of both breasts, and wherein said context data comprises information about the other breast.

Claim 49

Dependent Claim 19 words

The system of claim 41 wherein said context data includes information from a previous mammogram for the same patient.

Claim 50

Dependent Claim 26 words

The system of claim 41 further comprising spatial bandpass and spatial wavelet filter means for enhancing the contours of said ROI before extracting said context data.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2004-06-11	2004-12-11	2005-06-12	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2008-06-11	2008-12-11	2009-06-12	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2012-06-11	2012-12-11	2013-06-12	$7700.00	Window Closed / Lapsed

System for automated detection of cancerous masses in mammograms

Abstract

Inventors

Bibliographic Details

Claims Analysis

References Cited (Backward Citations)

USPTO Annuity & Maintenance Windows

Assigned Categories & Tags

Assign Category Tag