2018
DOI: 10.4172/2229-8711.1000230
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Detection of Alzheimer’s Disease Using Fractional Edge Detection

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Cited by 5 publications
(5 citation statements)
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“…This project demonstrates a visual comparison of filtering capabilities in medical image enhancement. It has been proved that the fractional filter is better than the integer-order filter [ 6 ]. With the established role in cystic fibrosis and bronchiectasis, nebulized antibiotics are increasingly used to treat respiratory infections in critically invasive, mechanically ventilated adult patients.…”
Section: Introductionmentioning
confidence: 99%
“…This project demonstrates a visual comparison of filtering capabilities in medical image enhancement. It has been proved that the fractional filter is better than the integer-order filter [ 6 ]. With the established role in cystic fibrosis and bronchiectasis, nebulized antibiotics are increasingly used to treat respiratory infections in critically invasive, mechanically ventilated adult patients.…”
Section: Introductionmentioning
confidence: 99%
“…The image processing of medical images in the fractional order domain was also tackled in [34], while in [35], a genetic algorithm was used to optimize the fractional order edge detection to diagnose Alzheimer disease (AD). Another method to detect AD was also presented in [36] based on fractional-order edge detection of Magnetic Resonance Imaging (MRI) images. In [37], a new fractional-order mask was introduced for edge detection and was applied on tracing breast cancer mammograms images.…”
Section: Survey On Fractional-order Image Edge Detectionmentioning
confidence: 99%
“…In work [13], a study of the edge detection method based on the fractional derivative and Canny filter to determine information contained on the edges of digital images was carried out. In [14], the authors constructed fractional differential filters based on Grünwald-Letnikov definition to apply them in medical images. The proposed method showed superiority in comparison with Sobel and Prewitt filters.…”
Section: Introductionmentioning
confidence: 99%