Method and system for automated detection of clustered microcalcifications from digital mammograms

A method for automated clustered microcalcification detection, prior to classification of detections, by digital image processing in screening mammography, comprising:

• storing a digital representation of a mammogram;
• using an optimizing algorithm and a database of training images to obtain optimized parameter values;
• applying a filtering algorithm using said optimized parameters to said digital representation to obtain a filtered image essentially comprising suspected microcalcifications;
• obtaining from said filtered image an image essentially comprising single-pixel representations of said suspected microcalcifications;
• and
• grouping said single-pixel representations into clusters using said optimized parameters.

A method for automated clustered microcalcification detection in a mammogram image by digital image processing in screening mammography, comprising:

• locating clusters of microcalcifications in said mammogram image;
• extracting features of said clusters of microcalcifications;
• and
• classifying said clusters of microcalcifications as suspicious or non-suspicious using said features in a multi-layer perceptron neural network;
• wherein said step of classifying, comprises the steps of:
• multiplying at least one of said features by a weight wij, where i is an index representing the ith feature vector element of a feature vector x having N elements and j is an index representing a jth first layer node;
• using first layer nodes of said multi-layer perceptron neural network to calculate first layer outputs ƒj, said first layer outputs ƒj, being calculated according to the function:
• f j = tanh ⁡ ( ∑ i = 1 N ⁢ ( w i , j × x i ) )
• where xi comprises a computed feature vector element;
• using said first layer outputs in a smoothly varying output function to obtain a series of resulting values;
• and
• identifying a cluster associated with one of said resulting values as suspicious if said one resulting value satisfies a predetermined relationship with respect to a threshold value, or non-suspicious otherwise.

A method for automated clustered microcalcification detection in a digital representation of a mammogram by digital image processing, comprising:

• filtering said digital representation to obtain a filtered image comprising filtered image pixels comprising suspected microcalcifications;
• determining, for each said filtered image pixel, a threshold value derived from a predetermined portion of said digital representation, said predetermined portion corresponding to said filtered image pixel;
• and
• thresholding each said filtered image pixel with said threshold value derived from said predetermined portion of said digital representation corresponding to said filtered image pixel to obtain a thresholded image comprising microcalcifications.

An apparatus for automated clustered microcalcification detection, prior to classification of detections, by digital image processing in screening mammography comprising:

• means for storing a digital representation of a mammogram;
• means for optimizing parameter values using an optimizing algorithm and a database of training images;
• means for applying a filtering algorithm, using said optimized parameters, to said digital representation to obtain a filtered image essentially comprising suspected microcalcifications;
• means for obtaining from said filtered image an image essentially comprising single-pixel representations of said suspected microcalcifications;
• and
• means for grouping said single-pixel representations into clusters using said optimized parameters.

An apparatus for automated clustered microcalcification detection in a mammogram image by digital image processing in screening mammography comprising:

• means for locating clusters of microcalcifications in said digital representation;
• means for extracting features of said clusters of microcalcifications;
• and
• a classifier for classifying said clusters of microcalcifications as suspicious or non-suspicious using said features in a multi-layer perceptron neural network, said classifier further comprising:
• means for multiplying at least one of said features by a weight wij, where i is an index representing the ith feature vector element of a feature vector x having N elements and j is an index representing a jth first layer node;
• first layer nodes for calculating first layer outputs ƒj according to the function:
• f j = tanh ⁡ ( ∑ i = 1 N ⁢ ( w i , j × x i ) )
• where xi comprises a computed feature vector element;
• and
• smoothly varying output means operatively connected to said first layer nodes to produce a resulting value;
• and
• means for identifying a cluster associated with said resulting value as suspicious if said resulting value satisfies a predetermined relationship with respect to a threshold value, or non-suspicious otherwise.

A system for automated clustered microcalcification detection in a digital representation of a mammogram by digital image processing comprising:

• a filter for filtering said digital representation to generate a filtered image comprising filtered image pixels comprising suspected microcalcifications;
• means for determining, from said digital representation, a threshold value for each said filtered image pixel;
• and
• thresholding means for thresholding each said filtered image pixel with its respective threshold value to generate a thresholded image comprising microcalcifications.