Detection of Alzhemier’s Disease Using Fractional Edge Detection
Journal Title: IOSR journal of VLSI and Signal Processing - Year 2019, Vol 9, Issue 1
Abstract
The work consist of two phases. The first phase of the work aims at finding out the optimized value of the fraction used in fractional filtering for image enhancement techniques in digital image processing. The work is done on MATLAB platform. The work starts with a comparative study of fractional order filter and integer order kernel filters like Sobel and Prewitt filters, used for edge detection and boundary detection of various digital images. With the view of applying fractional filtering in medical images, the work is done by utilizing Magnetic Resonance Imaging (MRI). The noise performances of these filters are analyzed upon the addition of random Gaussian noise. The mean square error (MSE) and peak signal to noise ratio (PSNR) of the detected images are adopted as evaluation methods for comparison. The visual comparison of the filters capability in medical image enhancement is presented in this project. It has been proved that fractional filter outperforms integer order filters. In the second phase fractional filtering with the optimized value of the fraction is utilized for the detection of Alzheimer disease (AD) from MRI scan of the brain. Based on MSE and PSNR optimized value for the fraction used in fractional filtering is found out to be 0.5. The fractional filter with fraction equal to 0.5 is used to detect Alzheimer’s disease. This could progressively help in understanding and treating Alzheimer’s disease.
Authors and Affiliations
Reju John, Nissan Kunju
Keywords
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