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Novel Methods for Improved Fusion of Medical Images

Alenezi, Fayadh S
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098

Abstract Details

2019, Doctor of Philosophy, University of Toledo, Electrical Engineering.
Medical image fusion (MIF) is a key technique for the analysis of diagnostic images in clinical applications. MIF aims to reduce uncertainty and redundancy derived from examining two or more multi-mode separate images, by creating one single composite image that is more useful for human interpretation. However, current MIF techniques have not successfully addressed the poor textual properties and deficient edge formation of many resulting images. In order to address these shortcomings, this dissertation proposes a variety of algorithms aimed at exploiting different combinations of well-known image processing and fusion techniques. The first algorithm exploits the properties of Gabor filtering and links maximum pixel selection with fuzzy-based image fusion, in order to improve the textual and edge properties of the fused medical images. The second algorithm focuses on reducing defects associated with single images created from different modalities by combining the action of Gabor filtering, maximum pixel intensity selection and Pulse Coupled Neural Network (PCNN) implementation. The third algorithm seeks to increase image information content and provide a complementary context for anatomical and physiological information by using a space-variant Wiener filter followed by image enhancement with lateral inhibition and excitation in a feature-linking PCNN under maximized normalization, and then fusion using a shift-invariant discrete wavelet transform (SIDWT). The fourth algorithm focuses on increasing the quality of the source images through a preprocessing technique which uses a greedy-iterative strategy for local contrast enhancement in order to minimize global image variance, together with global and local image contrast optimization based on the human visual system and a standard fusion algorithm. The fifth algorithm attains fusion in the Discrete Cosine Transform (DCT) domain under a novel Block Toeplitz matrix designed to enhance the finer details of all input images, followed by contrast adjustment and smoothing by bilateral filters using Gaussian kernels. All the novel MIF methods are discussed, thoroughly described, applied to a set of medical images and then evaluated and compared to existing fusion algorithms in terms of three objective measurements, namely pixel standard deviation, root-mean square error and image entropy. Most of these performance figures show significant improvements when compared to the reference fusion methods, thus suggesting that the newly developed algorithms represent a valuable contribution towards progress in this important application field.
Ezzatollah Salari (Committee Chair)
Mansoor Alam (Committee Member)
Junghwan Kim (Committee Member)
Richard Molyet (Committee Member)
Eddie Chou (Committee Member)
169 p.
Engineering

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Citations

  • Alenezi, F. S. (2019). Novel Methods for Improved Fusion of Medical Images [Doctoral dissertation, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098

    APA Style (7th edition)

  • Alenezi, Fayadh. Novel Methods for Improved Fusion of Medical Images. 2019. University of Toledo, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098.

    MLA Style (8th edition)

  • Alenezi, Fayadh. "Novel Methods for Improved Fusion of Medical Images." Doctoral dissertation, University of Toledo, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098

    Chicago Manual of Style (17th edition)

toledo1555170158084098
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© 2019, some rights reserved.Creative Commons License Novel Methods for Improved Fusion of Medical Images by Fayadh S Alenezi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.