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Connection

Junji Shiraishi to Image Processing, Computer-Assisted

This is a "connection" page, showing publications Junji Shiraishi has written about Image Processing, Computer-Assisted.
  1. Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning. Radiol Phys Technol. 2024 Jun; 17(2):360-366.
    View in: PubMed
    Score: 0.598
  2. [Image Evaluation with Paired Comparison Method Using Automatic Analysis Software: Comparison of CT Images with Simulated Levels of Exposure Dose]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2019; 75(1):32-39.
    View in: PubMed
    Score: 0.418
  3. C-programing for Image Processing: Introduction of the Experience-based E-learning. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2016; 72(10):1049-1051.
    View in: PubMed
    Score: 0.340
  4. Modulation transfer function measurement of CT images by use of a circular edge method with a logistic curve-fitting technique. Radiol Phys Technol. 2015 Jan; 8(1):53-9.
    View in: PubMed
    Score: 0.309
  5. A simple method for identifying image orientation of chest radiographs by use of the center of gravity of the image. Radiol Phys Technol. 2012 Jul; 5(2):207-12.
    View in: PubMed
    Score: 0.263
  6. [Development of a computer-aided diagnosis system for the distinction between benign and malignant gastric lesions]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2012; 68(11):1474-85.
    View in: PubMed
    Score: 0.258
  7. [Development of the computerized scheme for reconstructing three dimensional ultrasonography from the conventional two dimensional dynamic images]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2012; 68(12):1617-23.
    View in: PubMed
    Score: 0.258
  8. [Computerized estimation of a percent glandular tissue composition in computed radiography mammography]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2011; 67(12):1540-7.
    View in: PubMed
    Score: 0.240
  9. Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network. Radiol Phys Technol. 2024 Mar; 17(1):83-92.
    View in: PubMed
    Score: 0.146
  10. Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2016 02; 72(2):149-56.
    View in: PubMed
    Score: 0.085
  11. A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study. Radiol Phys Technol. 2016 Jan; 9(1):44-52.
    View in: PubMed
    Score: 0.083
  12. Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography. Med Phys. 2008 May; 35(5):1734-46.
    View in: PubMed
    Score: 0.050
  13. Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med Phys. 2003 Oct; 30(10):2584-93.
    View in: PubMed
    Score: 0.036
  14. [Development of an image processing scheme for chest radiographs using a dot printer]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2002 Sep; 58(9):1268-77.
    View in: PubMed
    Score: 0.034
  15. Computer-aided diagnosis with radiogenomics: analysis of the relationship between genotype and morphological changes of the brain magnetic resonance images. Radiol Phys Technol. 2018 Sep; 11(3):265-273.
    View in: PubMed
    Score: 0.025
  16. Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms. Med Phys. 2008 Dec; 35(12):5695-702.
    View in: PubMed
    Score: 0.013
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.