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A knowledge-based image enhancement and denoising approach

Author

Listed:
  • Hafiz Syed Muhammad Muslim

    (National University of Science and Technology)

  • Sajid Ali Khan

    (Foundation University Islamabad
    SZABIST)

  • Shariq Hussain

    (Foundation University Islamabad)

  • Arif Jamal

    (Foundation University Islamabad)

  • Hafiz Syed Ahmed Qasim

    (National University of Science and Technology)

Abstract

The emergence of computer-aided diagnostic technology has revolutionized the health sector and by use of medical imaging records, health experts are able to get detailed analysis which enable them in precise diagnosis of gliomas tumors. In this paper, we present an approach that uses domain-specific knowledge together with hybrid image enhancement techniques that provides resulting image(s) with more details and lesser noise levels. We did comparison of our KB proposed approach with existing techniques and the experimentation results showed improvement in quality and reduction of arbitrariness of images. The approach is proved to be feasible and effective, thus resulting in better medical diagnosis and evaluation of gliomas problems. Proposed research work recommends a new approach for medical imaging enhancements.

Suggested Citation

  • Hafiz Syed Muhammad Muslim & Sajid Ali Khan & Shariq Hussain & Arif Jamal & Hafiz Syed Ahmed Qasim, 2019. "A knowledge-based image enhancement and denoising approach," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 108-121, June.
    • Handle: RePEc:spr:comaot:v:25:y:2019:i:2:d:10.1007_s10588-018-9274-8
    DOI: 10.1007/s10588-018-9274-8
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    Cited by:

    1. Chenping Zhao & Wenlong Yue & Jianlou Xu & Huazhu Chen, 2023. "Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    2. Peter Chondro & Qazi Mazhar ul Haq & Shanq-Jang Ruan & Lieber Po-Hung Li, 2020. "Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs," Mathematics, MDPI, vol. 8(5), pages 1-15, May.

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