Literature DB >> 10565509

Computer-aided diagnosis in radiology: potential and pitfalls.

K Doi1, H MacMahon, S Katsuragawa, R M Nishikawa, Y Jiang.   

Abstract

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.

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Year:  1999        PMID: 10565509     DOI: 10.1016/s0720-048x(99)00016-9

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  29 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.  Computer-aided diagnosis for contrast-enhanced ultrasound in the liver.

Authors:  Katsutoshi Sugimoto; Junji Shiraishi; Fuminori Moriyasu; Kunio Doi
Journal:  World J Radiol       Date:  2010-06-28

3.  Radiological technologists' performance for the detection of malignant microcalcifications in digital mammograms without and with a computer-aided detection system.

Authors:  Rie Tanaka; Miho Takamori; Yoshikazu Uchiyama; Junji Shiraishi
Journal:  J Med Imaging (Bellingham)       Date:  2015-05-27

4.  Impact of breast density on computer-aided detection in full-field digital mammography.

Authors:  Silvia Obenauer; Christian Sohns; Carola Werner; Eckhardt Grabbe
Journal:  J Digit Imaging       Date:  2006-09       Impact factor: 4.056

5.  Comparison of algorithms to enhance spicules of spiculated masses on mammography.

Authors:  Mehul P Sampat; Gary J Whitman; Alan C Bovik; Mia K Markey
Journal:  J Digit Imaging       Date:  2008-03       Impact factor: 4.056

Review 6.  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

7.  Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

Authors:  Qi Guo; Jiaqing Shao; Virginie F Ruiz
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

Review 8.  Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

Authors:  Feng Li
Journal:  Radiol Phys Technol       Date:  2015-05-17

9.  Using breast radiographers' reports as a second opinion for radiologists' readings of microcalcifications in digital mammography.

Authors:  R Tanaka; M Takamori; Y Uchiyama; R M Nishikawa; J Shiraishi
Journal:  Br J Radiol       Date:  2014-12-23       Impact factor: 3.039

10.  Morphology filter bank for extracting nodular and linear patterns in medical images.

Authors:  Ryutaro Hashimoto; Yoshikazu Uchiyama; Keiichi Uchimura; Gou Koutaki; Tomoki Inoue
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-11-17       Impact factor: 2.924

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