Title
Potential usefulness of multiple-mammographic views in computer-aided diagnosis scheme for identifying histological classification of clustered microcalcification
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.
Year
DOI
Venue
2006
10.1007/11783237_32
Digital Mammography / IWDM
Keywords
Field
DocType
multiple-view mammograms,classification accuracy,potential usefulness,histological classification,modified bayes,noninvasive carcinoma,noncomedo type,computer-aided diagnosis scheme,comedo type,account image feature,computerized scheme,multiple-mammographic view,cc image,discriminant function,image features
Solitary pulmonary nodule,Pattern recognition,Microcalcification,Feature (computer vision),Computer-aided diagnosis,Artificial intelligence,Magnification,Comedo,Radiology,Quadratic discriminant function,Mathematics,Discriminant function analysis
Conference
Volume
ISSN
ISBN
4046
0302-9743
3-540-35625-8
Citations 
PageRank 
References 
0
0.34
3
Authors
7
Name
Order
Citations
PageRank
Ryohei Nakayama1144.80
Ryoji Watanabe201.01
Kiyoshi Namba321.38
Koji Yamamoto452.09
Kan Takeda500.68
Shigehiko Katsuragawa617226.20
Kunio Doi724211.74