Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection
This work addresses pre-cancer detection in cervical tissues, but it appears incremental as it applies existing wavelet and multifractal techniques to a specific medical imaging dataset.
The paper tackled pre-cancer detection by analyzing tissue images using wavelet and multifractal methods, finding that the Hurst exponent decreases from healthy to pre-cancer tissues and singularity spectrum width degrades at grade-I but increases with progression.
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.