University of Toledo
Alenezi, Fayadh S.
Novel Methods for Improved Fusion of Medical Images.
Degree: PhD, Electrical Engineering, 2019, University of Toledo
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.
Advisors/Committee Members: Salari , Ezzatollah (Committee Chair).
to Zotero / EndNote / Reference
APA (6th Edition):
Alenezi, F. S. (2019). Novel Methods for Improved Fusion of Medical Images. (Doctoral Dissertation). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098
Chicago Manual of Style (16th Edition):
Alenezi, Fayadh S. “Novel Methods for Improved Fusion of Medical Images.” 2019. Doctoral Dissertation, University of Toledo. Accessed September 19, 2019.
MLA Handbook (7th Edition):
Alenezi, Fayadh S. “Novel Methods for Improved Fusion of Medical Images.” 2019. Web. 19 Sep 2019.
Alenezi FS. Novel Methods for Improved Fusion of Medical Images. [Internet] [Doctoral dissertation]. University of Toledo; 2019. [cited 2019 Sep 19].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098.
Council of Science Editors:
Alenezi FS. Novel Methods for Improved Fusion of Medical Images. [Doctoral Dissertation]. University of Toledo; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555170158084098