Potential usefulness of multiple-mammographic views in computer-aided diagnosis scheme for identifying histological classification of clustered microcalcification

  • Authors:

  • Ryohei Nakayama;Ryoji Watanabe;Kiyoshi Namba;Koji Yamamoto;Kan Takeda;Shigehiko Katsuragawa;Kunio Doi

  • Affiliations:

  • Department of Radiology, Mie University School of Medicine, Tsu, Japan;Hakuaikai Hospital, Fukuoka, Japan;Breastopia Namba Hospital, Miyazaki, Japan;Medical Informatics Section, Mie University School of Medicine, Tsu, Japan;Department of Radiology, Mie University School of Medicine, Tsu, Japan;Department of Health Sciences, Kumamoto University School of Medicine, Kumamoto, Japan;Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Chicago, Illinois

  • Venue:
  • IWDM'06 Proceedings of the 8th international conference on Digital Mammography
  • Year:
  • 2006

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Abstract

The purpose of this study was to investigate the usefulness of multiple-view mammograms in the computerized scheme for identifying histological classifications. Our database consisted of mediolateral oblique (MLO) and craniocaudal (CC) magnification mammograms obtained from 77 patients, which included 14 invasive carcinomas, 17 noninvasive carcinomas of comedo type, 17 noninvasive carcinomas of noncomedo type, 14 mastopathies, and 15 fibroadenomas. Five features on clustered microcalcifications were determined from each of MLO and CC images by taking into account image features that experienced radiologists commonly use to identify histological classifications. Modified Bayes discriminant function (MBDF) was employed for distinguishing between histological classifications. For the input of MBDF, we used five or ten features obtained from MLO and/or CC images. With ten features, the classification accuracies for each histological classification ranged from 70.6% to 93.3%. This result was higher than that obtained with only five features either from MLO or CC images.