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

TL;DR

This study employs wavelet coherency and multifractal analysis on DIC images to identify pre-cancerous tissue changes, revealing significant variations in fractal parameters across different tissue grades.

Contribution

The paper introduces a wavelet-based multifractal approach for quantifying tissue changes in pre-cancer detection using DIC images, providing new insights into tissue heterogeneity.

Findings

Hurst exponent decreases from normal to pre-cancer tissues

Singularity spectrum width drops at grade-I and increases in advanced grades

Wavelet coherence reveals correlation levels between tissue types

Abstract

In this paper, we have carried out the detail studies of pre-cancer by wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA) on differential interference contrast (DIC) images of stromal region among different grades of pre-cancer tissues. Discrete wavelet transform (DWT) through Daubechies basis has been performed for identifying fluctuations over polynomial trends for clear characterization and differentiation of tissues. Wavelet coherence plots are performed for identifying the level of correlation in time scale plane between normal and various grades of DIC samples. Applying MFDFA on refractive index variations of cervical tissues, we have observed that the values of Hurst exponent (correlation) decreases from healthy (normal) to pre-cancer tissues. The width of singularity spectrum has a sudden degradation at grade-I in comparison of healthy (normal) tissue but later on it increases as cancer progresses from grade-II to grade-III.