Synarc Inc

Marco Loog

Mads Nielsen

Jakob Raundahl

Gopal Raghunath Karemore

Sami Sebastian Brandt

Mammogram images are processed by computer to derive automatically a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being a parameter that changes alongside changes in breast density and is hence useful in assessing cancer risk. The method comprises the steps of processing each image of at least part of a breast by: computing for pixels of the image a quotient value representative of the aspect ratio of tissue structures depicted in the image; using a trained classifier to classify said pixels according to their respective said quotient values and assigning a score to the respective pixels representing their classification with respect to at least two classes; deriving said parameter that changes alongside changes in breast density based on the aggregate pixel membership scores of said classes. The classifier may be trained either by unsupervised learning or by supervised learning.

Breast tissue density measure

A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Saud features may include the 3-jet of said pixels.

A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Said features may include the 3-jet of said pixels.

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Rotterdam, Netherlands

Marco Loog