DeepAI AI Chat
Log In Sign Up

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

by   Sabyasachi Mukhopadhyay, et al.

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.


Res-Dense Net for 3D Covid Chest CT-scan classification

One of the most contentious areas of research in Medical Image Preproces...

Wavelets and continuous wavelet transform for autostereoscopic multiview images

Recently, the reference functions for the synthesis and analysis of the ...

Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images

The hyperdense middle cerebral artery (MCA) dot sign has been reported a...

iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform

With the rapid development of whole brain imaging technology, a large nu...

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images

The world is currently experiencing an ongoing pandemic of an infectious...

Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection

In this paper, we have carried out the detail studies of pre-cancer by w...