Potential Usefulness of Multiple-Mammographic Views in Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcification

  • Ryohei Nakayama
  • Ryoji Watanabe
  • Kiyoshi Namba
  • Koji Yamamoto
  • Kan Takeda
  • Shigehiko Katsuragawa
  • Kunio Doi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)


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.


Classification Accuracy Invasive Carcinoma Histological Classification Solitary Pulmonary Nodule Quadratic Discriminant Function 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryohei Nakayama
    • 1
  • Ryoji Watanabe
    • 2
  • Kiyoshi Namba
    • 3
  • Koji Yamamoto
    • 4
  • Kan Takeda
    • 1
  • Shigehiko Katsuragawa
    • 5
  • Kunio Doi
    • 6
  1. 1.Department of RadiologyMie University School of MedicineTsuJapan
  2. 2.Hakuaikai HospitalFukuokaJapan
  3. 3.Breastopia Namba HospitalMiyazakiJapan
  4. 4.Medical Informatics SectionMie University School of MedicineTsuJapan
  5. 5.Department of Health SciencesKumamoto University School of MedicineKumamotoJapan
  6. 6.Kurt Rossmann Laboratories for Radiologic Image Research, Department of RadiologyThe University of ChicagoChicago

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