Abstract
In this work, we report a wavelet based multi-fractal study of images of dysplastic and neoplastic HE- stained
human cervical tissues captured in the transmission mode when illuminated by a laser light (He-Ne 632.8nm
laser). It is well known that the morphological changes occurring during the progression of diseases like cancer
manifest in their optical properties which can be probed for differentiating the various stages of cancer. Here,
we use the multi-resolution properties of the wavelet transform to analyze the optical changes. For this, we have
used a novel laser imagery technique which provides us with a composite image of the absorption by the different
cellular organelles. As the disease progresses, due to the growth of new cells, the ratio of the organelle to cellular
volume changes manifesting in the laser imagery of such tissues. In order to develop a metric that can quantify
the changes in such systems, we make use of the wavelet-based fluctuation analysis. The changing self- similarity
during disease progression can be well characterized by the Hurst exponent and the scaling exponent. Due to the
use of the Daubechies' family of wavelet kernels, we can extract polynomial trends of different orders, which help
us characterize the underlying processes effectively. In this study, we observe that the Hurst exponent decreases
as the cancer progresses. This measure could be relatively used to differentiate between different stages of cancer
which could lead to the development of a novel non-invasive method for cancer detection and characterization.
