title: Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms.
abstract: 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 scheme for 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.
authors.affiliation: Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu-shi 514-8507, Japan. nakayama@clin.medic.mie-u.ac.jp
authors.forename: Ryohei
authors.initials: R
authors.lastname: Nakayama
authors.forename: Yoshikazu
authors.initials: Y
authors.lastname: Uchiyama
authors.forename: Ryoji
authors.initials: R
authors.lastname: Watanabe
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Namba
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001941:Breast Diseases / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D001943:Breast Neoplasms / Q000145:classification* / Q000150:complications / Q000000981:diagnostic imaging* / Q000473:pathology; D002114:Calcinosis / Q000145:classification* / Q000150:complications / Q000000981:diagnostic imaging* / Q000473:pathology; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity