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A knowledge-based image enhancement and denoising approach

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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.

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References

  • Basics of X-ray physics—tissue densities. Radiology Masterclass. http://www.radiologymasterclass.co.uk/tutorials/physics/x-ray_physics_densities. Accessed July 2017

  • Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics

  • Dong AS, Lou BB, Jiang HY, Tong Q, Yang GM, Gao TH, Yang BQ, Zhang LB (2013) Medical image enhancement method based on wavelet transform and mode decomposition. Appl Mech Mater 389:930–935

    Article  Google Scholar 

  • Du J, Li W, Xiao B, Nawaz Q (2016) Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194:326–339

    Article  Google Scholar 

  • Haddad RA, Akansu AN (1991) A class of fast Gaussian binomial filters for speech and image processing. IEEE Trans Signal Process 39(3):723–727

    Article  Google Scholar 

  • Hossain MF, Alsharif MR, Yamashita K (2010) Medical image enhancement based on nonlinear technique and logarithmic transform coefficient histogram matching. In: The 2010 IEEE/ICME international conference on complex medical engineering, Gold Coast, Australia, 2010

  • Hum YC, Lai KW, Salim MIM (2014) Multiobjectives bihistogram equalization for image contrast enhancement. Complexity 20(2):22–36

    Article  Google Scholar 

  • Kaur R, Kaur S (2016) Comparison of contrast enhancement techniques for medical image. In: Conference on emerging devices and smart systems, Namakkal, 2016

  • Khan SA, Hussain A, Usman M (2016) Facial expression recognition on real world face images using intelligent techniques: a survey. Optik Int J Light Electron Opt 127(15):6195–6203

    Article  Google Scholar 

  • Khan SA, Hussain A, Usman M (2017) Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features. Multimed Tools Appl 77:1–33

    Google Scholar 

  • Mahmoud HS, Al-Ani MS (2013) Medical image enhancement based on an efficient approach for adaptive anisotropic diffusion. Int J Adv Eng Technol 6(3):1424

    Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  • Marieb EN (1989) Human anatomy and physiology. Benjamin-Cummings Publishing Company, Redwood City

    Google Scholar 

  • Morphological dilation and erosion (2017). Mathworks. https://www.mathworks.com/help/images/morphological-dilation-and-erosion.html. Accessed 25 Aug 2017

  • Munir A, Hussain A, Khan SA, Nadeem M, Arshid S (2018) Illumination invariant facial expression recognition using selected merged binary patterns for real world images. Optik 158:1016–1025

    Article  Google Scholar 

  • Nixon M, Aguado SA (2008) Feature extraction and image processing. Elsevier, Amsterdam

    Google Scholar 

  • Photometrics—high-performance CMOS, EMCCD and CCD cameras. Photometrics. https://www.photometrics.com/resources/learningzone/signaltonoiseratio.php. Accessed 19 Aug 2017

  • Rahman MA, Liu S, Wong CY, Lin SC-F, Liu SC, Kwok NM (2017) Multi-focal image fusion using degree of focus and fuzzy logic. Digit Signal Process 60:1–19

    Article  Google Scholar 

  • Sajjad M, Muhammad K, Baik SW, Rho S, Jan Z, Yeo S-S, Mehmood I (2017a) Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices. Multimed Tools Appl 76(3):3519–3536

    Article  Google Scholar 

  • Sajjad M, Khan S, Jan Z, Muhammad K, Kwak JT, Rho S, Baik SW, Mehmood I (2017b) Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 5:3475–3489

    Article  Google Scholar 

  • Subudhi BN, Thangaraj V, Sankaralingam E, Ghosh A (2016) Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation. Magn Reson Imaging 34(9):1292–1304

    Article  Google Scholar 

  • Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional, Inc., San Diego, p 474–485

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Authors and Affiliations

  1. Department of Software Engineering, Military College of Signals (MCS), National University of Science and Technology, Islamabad, Pakistan

    Hafiz Syed Muhammad Muslim

  2. Department of Software Engineering, Foundation University Islamabad, Rawalpindi Campus, Islamabad, Pakistan

    Sajid Ali Khan, Shariq Hussain & Arif Jamal

  3. Department of Computer Science, SZABIST, Islamabad, Pakistan

    Sajid Ali Khan

  4. Department of Computing, School of Electrical Engineering and Computer Science (SEECS), National University of Science and Technology, Islamabad, Pakistan

    Hafiz Syed Ahmed Qasim

Authors
  1. Hafiz Syed Muhammad Muslim
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  2. Sajid Ali Khan
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  3. Shariq Hussain
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  4. Arif Jamal
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  5. Hafiz Syed Ahmed Qasim
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Correspondence to Arif Jamal.

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Muslim, H.S.M., Khan, S.A., Hussain, S. et al. A knowledge-based image enhancement and denoising approach. Comput Math Organ Theory 25, 108–121 (2019). https://doi.org/10.1007/s10588-018-9274-8

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  • DOI: https://doi.org/10.1007/s10588-018-9274-8

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