Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain

TL;DR

This paper investigates heterogeneity in human brain CT scan images by applying wavelet and multi-fractal analysis, revealing spatial domain mismatches through systematic de-noising and unfolding techniques.

Contribution

It introduces a combined wavelet and MFDFA approach to diagnose heterogeneity in brain CT images, highlighting differences between vertical and horizontal analyses.

Findings

Mismatch between vertical and horizontal unfolding results

Heterogeneity confirmed by wavelet normalized energy and semi-log plots

Differences in Hurst exponent and singularity spectrum widths

Abstract

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.