Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain
This work provides a method for diagnosing heterogeneous dynamics in brain CT scans, which could aid in medical imaging analysis, but it is incremental as it applies existing techniques to a specific dataset.
The study analyzed CT scan images of a human brain using wavelet transform and multi-fractal analysis, revealing a mismatch between vertical and horizontal unfolding results that confirms heterogeneity in spatial dynamics.
CT scan images of human brain of a particular patient in different cross sections are taken, on which wavelet transform and multi-fractal analysis are applied. The vertical and horizontal unfolding of images are done before analyzing these images. A systematic investigation of de-noised CT scan images of human brain in different cross-sections are carried out through wavelet normalized energy and wavelet semi-log plots, which clearly points out the mismatch between results of vertical and horizontal unfolding. The mismatch of results confirms the heterogeneity in spatial domain. Using the multi-fractal de-trended fluctuation analysis (MFDFA), the mismatch between the values of Hurst exponent and width of singularity spectrum by vertical and horizontal unfolding confirms the same.