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Computer‐aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms
Author(s) -
Nakayama Ryohei,
Uchiyama Yoshikazu,
Watanabe Ryoji,
Katsuragawa Shigehiko,
Namba Kiyoshi,
Doi Kunio
Publication year - 2004
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 - 10.1118/1.1655711
Subject(s) - magnification , mammography , medical imaging , classification scheme , computer aided diagnosis , artificial intelligence , pattern recognition (psychology) , scheme (mathematics) , contextual image classification , computer science , computer vision , radiology , medicine , mathematics , breast cancer , cancer , image (mathematics) , information retrieval , mathematical analysis
The histological classification of clustered microcalcifications on mammograms can be difficult, and thus often require biopsy or follow‐up. Our purpose in this study was to develop a computer‐aided diagnosis schemefor identifying the histological classification of clustered microcalcifications on magnification mammograms in order to assist the radiologists' interpretation as a “second opinion.” Our database consisted of 58 magnification mammograms, which included 35 malignant clustered microcalcifications (9 invasive carcinomas, 12 noninvasive carcinomas of the comedo type, and 14 noninvasive carcinomas of the noncomedo type) and 23 benign clustered microcalcifications (17 mastopathies and 6 fibroadenomas). The histological classifications of all clustered microcalcifications were proved by pathologic diagnosis. The clustered microcalcifications were first segmented by use of a novel filter bank and a thresholding technique. Five objective features on clustered microcalcifications were determined by taking into account subjective features that experienced the radiologists commonly use to identify possible histological classifications. The Bayes decision rule with five objective features was employed for distinguishing between five histological classifications. The classification accuracies for distinguishing between three malignant histological classifications were 77.8% (7/9) for invasive carcinoma, 75.0% (9/12) for noninvasive carcinoma of the comedo type, and 92.9% (13/14) for noninvasive carcinoma of the noncomedo type. The classification accuracies for distinguishing between two benign histological classifications were 94.1% (16/17) for mastopathy, and 100.0% (6/6) for fibroadenoma. This computerized method would be useful in assisting radiologists in their assessments of clustered microcalcifications.