Literature DB >> 17821997

Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings.

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

Abstract

The presentation of images that are similar to that of an unknown lesion seen on a mammogram may be helpful for radiologists to correctly diagnose that lesion. For similar images to be useful, they must be quite similar from the radiologists' point of view. We have been trying to quantify the radiologists' impression of similarity for pairs of lesions and to establish a "gold standard" for development and evaluation of a computerized scheme for selecting such similar images. However, it is considered difficult to reliably and accurately determine similarity ratings, because they are subjective. In this study, we compared the subjective similarities obtained by two different methods, an absolute rating method and a 2-alternative forced-choice (2AFC) method, to demonstrate that reliable similarity ratings can be determined by the responses of a group of radiologists. The absolute similarity ratings were previously obtained for pairs of masses and pairs of microcalcifications from five and nine radiologists, respectively. In this study, similarity ranking scores for eight pairs of masses and eight pairs of microcalcifications were determined by use of the 2AFC method. In the first session, the eight pairs of masses and eight pairs of microcalcifications were grouped and compared separately for determining the similarity ranking scores. In the second session, another similarity ranking score was determined by use of mixed pairs, i.e., by comparison of the similarity of a mass pair with that of a calcification pair. Four pairs of masses and four pairs of microcalcifications were grouped together to create two sets of eight pairs. The average absolute similarity ratings and the average similarity ranking scores showed very good correlations in the first study (Pearson's correlation coefficients: 0.94 and 0.98 for masses and microcalcifications, respectively). Moreover, in the second study, the correlations between the absolute ratings and the ranking scores were also very high (0.92 and 0.96), which implies that the observers were able to compare the similarity of a mass pair with that of a calcification pair consistently. These results provide evidence that the concept of similarity for pairs of images is robust, even across different lesion types, and that radiologists are able to reliably determine subjective similarity for pairs of breast lesions.

Entities:  

Mesh:

Year:  2007        PMID: 17821997     DOI: 10.1118/1.2745937

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


  15 in total

1.  An interactive system for computer-aided diagnosis of breast masses.

Authors:  Xingwei Wang; Lihua Li; Wei Liu; Weidong Xu; Dror Lederman; Bin Zheng
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

2.  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

3.  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

4.  Optimization of reference library used in content-based medical image retrieval scheme.

Authors:  Sang Cheol Park; Rahul Sukthankar; Lily Mummert; Mahadev Satyanarayanan; Bin Zheng
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

5.  Observer study for evaluating potential utility of a super-high-resolution LCD in the detection of clustered microcalcifications on digital mammograms.

Authors:  Junji Shiraishi; Hiroyuki Abe; Katsuhiro Ichikawa; Robert A Schmidt; Kunio Doi
Journal:  J Digit Imaging       Date:  2009-03-10       Impact factor: 4.056

6.  Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment.

Authors:  Xiao-Hui Wang; Sang Cheol Park; Bin Zheng
Journal:  Phys Med Biol       Date:  2009-01-16       Impact factor: 3.609

7.  Medical display application for degraded image sharpness restoration based on the modulation transfer function: initial assessment for a five-megapixel mammography display monitor.

Authors:  Shogo Tokurei; Yoichiro Ikushima; Kazuki Takegami; Munemasa Okada; Junji Morishita
Journal:  Phys Eng Sci Med       Date:  2021-05-17

Review 8.  Overview of deep learning in medical imaging.

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

9.  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

10.  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
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.