fractional order derivative

This study explores the application of a fractional diffusion equation in diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) analysis, aiming to validate and extend previous research based on an open-access dataset. A fractional-order model using the Mittag-Leffler function is implemented and validated by reproducing results presented in existing literature. The method is then applied to an open-access Connectome Diffusion Microstructure Dataset (CDMD) to analyze real brain imaging data.

This paper presents an innovative approach to blind image deblurring based on fractional order derivatives and Nash game theory.  The integration of fractional order derivatives enhances the deblurring process, capturing intricate image details beyond the capabilities of traditional integer-order derivatives.  The Nash game framework is employed to model the strategic interaction between the image and the unknown blur kernel, fostering a cooperative optimization process.  Experimental results showcase the proposed method's superiority in terms of both Peak Signal-to-Noi

This paper explores the application of the U-Net architecture for intracranial hemorrhage segmentation, with a focus on enhancing segmentation accuracy through the incorporation of texture enhancement techniques based on the Riesz fractional order derivatives. The study begins by conducting a review of related works in the field of computed tomography (CT) scan segmentation. At this stage also a suitable dataset is selected.