Literature DB >> 17022242

Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: observer study results.

Chisako Muramatsu1, Qiang Li, Robert Schmidt, Kenji Suzuki, Junji Shiraishi, Gillian Newstead, Kunio Doi.   

Abstract

Presentation of images of lesions similar to that of an unknown lesion might be useful to radiologists in distinguishing between benign and malignant clustered microcalcifications on mammograms. Investigators have been developing computerized schemes to select similar images from large databases. However, whether selected images are really similar in appearance is not examined for most of the schemes. In order to retrieve images that are useful to radiologists, the selected images must be similar from radiologists' diagnostic points of view. Therefore, in this study, the data of radiologists' subjective similarity for pairs of clustered microcalcification images were obtained from a number of observers, and the intra- and inter-observer variations and the intergroup correlations were determined to investigate whether reliable similarity ratings by human observers can be determined. Nineteen images of clustered microcalcifications, each of which was paired with six other images, were selected for the observer study. Thus, subjective similarity ratings for 114 pairs of clustered microcalcifications were determined by each observer. Thirteen breast, ten general, and ten nonradiologists participated in the observer study; some of them completed the study multiple times. Although the intraobserver variations for the individual readings and the interobserver variations for pairs of observers were not small, the interobserver agreements were improved by taking the average of readings by the same observers. When the similarity ratings by a number of observers were averaged among the groups of breast, general, and nonradiologists, the mean differences of the ratings between the groups decreased, and good concordance correlations (0.846, 0.817, and 0.785) between the groups were obtained. The result indicates that reliable similarity ratings can be determined by use of this method, and the average similarity ratings by breast radiologists can be considered meaningful and useful for the development and evaluation of a computerized scheme for selection of similar images.

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Year:  2006        PMID: 17022242     DOI: 10.1118/1.2266280

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  11 in total

1.  Computerized segmentation method for individual calcifications within clustered microcalcifications while maintaining their shapes on magnification mammograms.

Authors:  Akiyoshi Hizukuri; Ryohei Nakayama; Nobuo Nakako; Hiroharu Kawanaka; Haruhiko Takase; Koji Yamamoto; Shinji Tsuruoka
Journal:  J Digit Imaging       Date:  2012-06       Impact factor: 4.056

2.  Evaluation of objective similarity measures for selecting similar images of mammographic lesions.

Authors:  Ryohei Nakayama; Hiroyuki Abe; Junji Shiraishi; Kunio Doi
Journal:  J Digit Imaging       Date:  2011-02       Impact factor: 4.056

3.  Content-based image retrieval in radiology: analysis of variability in human perception of similarity.

Authors:  Jessica Faruque; Christopher F Beaulieu; Jarrett Rosenberg; Daniel L Rubin; Dorcas Yao; Sandy Napel
Journal:  J Med Imaging (Bellingham)       Date:  2015-04-03

Review 4.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

Review 5.  Overview of deep learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Radiol Phys Technol       Date:  2017-07-08

6.  Modeling perceptual similarity measures in CT images of focal liver lesions.

Authors:  Jessica Faruque; Daniel L Rubin; Christopher F Beaulieu; Sandy Napel
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

7.  Representation of lesion similarity by use of multidimensional scaling for breast masses on mammograms.

Authors:  Chisako Muramatsu; Kohei Nishimura; Tokiko Endo; Mikinao Oiwa; Misaki Shiraiwa; Kunio Doi; Hiroshi Fujita
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

8.  Usefulness of presentation of similar images in the diagnosis of breast masses on mammograms: comparison of observer performances in Japan and the USA.

Authors:  Chisako Muramatsu; Robert A Schmidt; Junji Shiraishi; Tokiko Endo; Hiroshi Fujita; Kunio Doi
Journal:  Radiol Phys Technol       Date:  2012-08-08

9.  Analysis of perceived similarity between pairs of microcalcification clusters in mammograms.

Authors:  Juan Wang; Hao Jing; Miles N Wernick; Robert M Nishikawa; Yongyi Yang
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

Review 10.  Overview on subjective similarity of images for content-based medical image retrieval.

Authors:  Chisako Muramatsu
Journal:  Radiol Phys Technol       Date:  2018-05-08
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