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A fractal approach to the segmentation of microcalcifications in digital mammograms
Author(s) -
Lefebvre Françoise,
Benali Habib,
Gilles René,
Kahn Edmond,
Di Paola Robert
Publication year - 1995
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.597473
Subject(s) - mammography , digital mammography , segmentation , fractal , artificial intelligence , digital radiography , computer vision , medical imaging , computer science , pattern recognition (psychology) , medical physics , medicine , radiology , mathematics , radiography , breast cancer , mathematical analysis , cancer
This paper presents a computerized method for the automated segmentation of individual microcalcifications in a region of interest (ROI) known to contain a cluster in digital mammograms. Mammographic parenchyma can be accurately modeled with the fractal approach, but not areas with microcalcifications. The digitized image is divided into 16×16‐pixel overlapping windows and those accurately modeled by the fractal model are eliminated. The next steps include local thresholding of the ROIs using an iterative method, the elimination of some of the artifacts and identification of the clustered microcalcifications using a clustering algorithm. The evaluation was performed on 81 simulated clusters superimposed on normal mammographic backgrounds and on a representative database of 408 real mammograms. Microcalcification locations were identified by two radiologists independently. These locations were compared to those found by the computer algorithm. An average of 59% of the simulated microcalcifications and 69% of the microcalcifications common to both radiologists were detected. The algorithm described provides a fully automated method for the segmentation of individual microcalcifications in an area of the mammogram known to contain a cluster.