Autors: Mironov, R. P., Kountchev, R. K.
Title: Architecture for Medical Image Processing
Keywords: Image Processing, Medical Imaging, Software Architectures, Knowledge Base Systems

Abstract: In this paper, software architecture for medical image processing, analysis and archiving is presented. On the basis of the considered architecture a new task-oriented medical image processing system, which allows imitating of the human visual system, is developed. The basic functions include input/output of halftone images, pre- and post-processing, filtration, compression, enhancement, 2D linear transforms, pseudo-color transforms, analysis and interpolations. Using the system features, various image processing tasks are semantically described in the experimental part. The main advantages of the proposed architecture are the use of adaptive algorithms for processing of medical images, tailored to their specific features.

References

    Issue

    Advances in Intelligent Analysis of Medical Data and Decision Support Systems, vol. 473, pp. pp.225-234, 2013, Switzerland, Springer International Publishing, ISBN: 978-3-319-00028-2/ISSN: 1860-949X/ DOI: 10.1007/978-3-319-00029-9_20

    Copyright Springer International Publishing

    Цитирания (Citation/s):
    1. Shkurat, O., Sulema, Y., Suschuk-Sliusarenko, V., Dychka, A. Image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set. 2nd International Conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2018; Moscow; Russian Federation; 6 October 2018 through 8 October 2018; Code 226259. Advances in Intelligent Systems and Computing, Volume 902, Springer Verlag, 2020, pp. 271-281. ISSN: 21945357, ISBN: 978-303012081-8, DOI: 10.1007/978-3-030-12082-5_25. - 2020 - в издания, индексирани в Scopus или Web of Science

    Вид: книга/глава(и) от книга, публикация в реферирано издание, индексирана в Scopus