الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Medical image processing has captured a large interest from the researchers in
the last decade. With the development of different medical imaging modalities, it has
become a must to find new image processing tools and algorithms that can help in the
automated medical image diagnosis task. Of such tools, medical image fusion finds
high popularity. Image fusion, in general, is the process of integrating information
existing in two or more images for the same scene in a single image. This concept has
been developed to some extent to include the fusion of multi-modality images in
medical applications. It is known that different medical imaging techniques work with
different principles, and hence they give images with different characteristics. That is
why the medical image fusion task is a complex task that needs image registration,
image dynamic range modification, and efficient fusion algorithms to integrate the
obtained images in the fusion results. This thesis is concerned with this complicated
task of medical image fusion. It covers the main concepts and algorithms utilized
form medical image fusion. In addition the quality metrics that can be used for the
assessment of medical image fusion results are also covered. The thesis presents two
proposed techniques to achieve better medical image fusion. The first technique is a
hybrid technique that merges wavelet-based image fusion with principle component
analysis image fusion. An adaptive strategy is adopted to switch between both
techniques according to the image local activity levels estimated with the local
variance. This proposed technique achieves better quality indices than those obtained
with the traditional techniques. The second proposed technique depends on the
utilization of optimization concepts in order to maximize the image quality metrics
after the fusion process. This technique comprises histogram processing, image
registration, and wavelet-based image fusion. It is carried out iteratively towards the
best solution from the quality metrics perspective. Simulation results prove the best
quality of the fusion process with the proposed technique that depends on
optimization with an acceptable processing time taken through the optimization
process. Finally, both suggested techniques can be used in a unified framework for
automated medical diagnosis that can help specialists to take the correct decision
according to the obtained high-quality fusion results.