The emission tomographic testing procedures are of radiation from the radioactive isotopes of radiological imaging techniques. The advantages of emission tomographic procedure in contrast to other medical images are that they are able to the morphological information separately in addition to the visualization server operations, and represent the differences in shape and function as well.
Several of diagnostic imaging procedures suffer from the Partial Volume Effect, because this phenomenon significantly degrades the image quality and this phenomenon causes several errors; e.g., where the anatomy formula is only partially filling the voxel there the image becomes blurred and the intensity value does not correspond to reality. This is because data of overlapping formulas are summed. The boundaries of overlapping formulas are difficult to separate from each other and smaller lesions are not diagnosed. For this errors mentioned above, it is important to correct this phenomenon. The Partial Volume Effect have been made to improve a several of attempts and several methods. During my degree work, I have dealth with a newly developed method based on the Reverse Diffusion algorithm. I implemented this algorithm in MATLAB environment.
The aim of Reverse Diffusion algorithm is to correct the Partial Volume Effect and corresponding reduction in medical imaging without creating artifact and without any loss in image quality. I think it is important to improve the different medical imaging pictures taken from the Partial Volume Effect, so I chose my degree work to develop this theme in my work. In my degree work, the aim was to create the Reverse Diffusion in the 1D, 2D and 3D, and to observe efficiency in my algorithm.