Joint optimization of parameters for the detection of clustered microcalcifications in digital mammograms

A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of:

• a) optimizing at least one value for a predetermined threshold parameter;
• b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications;
• c) thresholding the filtered image from step b) to produce a threshold image;
• d) forming clusters of microcalcifications in the filtered image from step c);
• and
• e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications;
• wherein said step a) comprises:
• i) providing an optimizing algorithm;
• ii) providing a database of training images;
• and
• iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined threshold parameters;
• and
• wherein said step c) comprises:
• i) providing a thresholding algorithm incorporating the optimized predetermined threshold parameter;
• and
• ii) applying the thresholding algorithm to the filtered image to identify potential microcalcifications.

A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of:

• a) optimizing a value for at least one predetermined clustering algorithm parameter;
• b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications;
• c) thresholding the filtered image from step b) to produce a thresholded image;
• d) forming clusters of microcalcifications in the thresholded image from step c);
• and
• e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications;
• wherein said step a) comprises:
• i) providing an optimizing algorithm;
• ii) providing a database of training images;
• and
• iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined clustering algorithm parameters;
• and
• wherein said step d) comprises:
• i) providing a clustering algorithm incorporating the optimized predetermined clustering algorithm parameter;
• and
• ii) applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications.

A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of:

• a) jointly optimizing a filtering algorithm parameter, a thresholding algorithm parameter and a clustering algorithm parameter;
• b) providing a filtering algorithm incorporating the optimized filtering algorithm parameter and applying the filtering algorithm to the digital mammogram image to produce a filtered image comprising essentially microcalcifications;
• c) providing a thresholding algorithm incorporating the optimized thresholding algorithm parameter and applying the thresholding algorithm to the filtered image to produce a thresholded image;
• d) providing a clustering algorithm incorporating the optimized clustering algorithm parameter and applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications;
• and
• e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from non-suspicious clusters of microcalcifications.