Tissue Image Classification Using Multi-Fractal Spectra

Abstract

Tissue image classification is a challenging problem due to the fact that the images contain highly irregular shapes in complex spatial arrangement. The multi-fractal formalism has been found useful in characterizing the intensity distribution present in such images, as it can effectively resolve local densities and also represent various structures present in the image. This paper presents a detailed study of feature vectors derived from the distribution of Holder exponents and the geometrical characteristics of the multi-fractal spectra that can be used in applications requiring image classification and retrieval. The paper also gives the results of experimental analysis performed using a tissue image database and demonstrates the effectiveness of the proposed multi-fractal-based descriptors in tissue image classification and retrieval. Implementation aspects that need to be considered for improving classification accuracy and the feature representation capability of the proposed descriptors are also outlined.

Application of multifractal analysis on microscopic images in the classification of metastatic bone disease

“…2.1 Applying multifractal analysis in research of cancer Many researches have shown existing correlation between fractal and multifractal parameters and the tumours, or pathology forms (Baish et al 1998;Budimlija 2001;Buldyrev et al 1994;Bulmore et al 1994;Bunde et al 1990Bunde et al , 1991Byng et al 1996;Fenoglio-Preiser et al 1989;Claridge et al 1992;Coffey 1998;Cross 1994Cross , 1997Reljin et al 2000Reljin et al , 2002Reljin et al , 2008Reljin et al , 2009Stojic et al 2006;Ballerini et al 2003;Roy et al 2011;Mukundan and Hemsley 2010). It has been shown that tissue images contain several fractal structures with varying dimension at varying strengths (Reljin et al 2000(Reljin et al , 2002Mukundan and Hemsley 2010).…”

“…It has been shown that tissue images contain several fractal structures with varying dimension at varying strengths (Reljin et al 2000(Reljin et al , 2002Mukundan and Hemsley 2010). MF analysis can be applied to gray-level images, in general.…”

“…Multifractal analysis is based on the assumption that one can define measures of local intensity distribution that scale according to a power law; in this case each different exponent represents a different fractal structure with its own fractal dimension (Mukundan and Hemsley 2010). Multifractal's main parameter is the Holder's exponent (Evertsz and Mandelbrot 1992;Turner et al 1998).…”

Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models

“…the local dimension) is known as the “Holder exponent” or α values [ 101 , 102 ]. In image data analysis, α values reflect the local behavior of the intensity measure that are of 4 different types [ 105 , 106 ]. The corresponding image is often referred to as the α –image.…”