Customized First and Second Order Statistics Based Operators to Support Advanced Texture Analysis of MRI Images

TLDR

Texture analysis mathematically describes image objects by extracting characteristics linked to visible or hidden properties, treating high‑quality medical image pixels as outcomes of stochastic processes, and motivating a statistical operator approach. The paper introduces customized first‑ and second‑order statistical operators for advanced MRI texture analysis. The operators are defined by rules that specify each operator’s role and its relationship with other operators. Experiments on diverse MRI datasets show the proposed operators are useful and accurate.

Abstract

Texture analysis is the process of highlighting key characteristics thus providing an exhaustive and unambiguous mathematical description of any object represented in a digital image. Each characteristic is connected to a specific property of the object. In some cases the mentioned properties represent aspects visually perceptible which can be detected by developing operators based on Computer Vision techniques. In other cases these properties are not visually perceptible and their computation is obtained by developing operators based on Image Understanding approaches. Pixels composing high quality medical images can be considered the result of a stochastic process since they represent morphological or physiological processes. Empirical observations have shown that these images have visually perceptible and hidden significant aspects. For these reasons, the operators can be developed by means of a statistical approach. In this paper we present a set of customized first and second order statistics based operators to perform advanced texture analysis of Magnetic Resonance Imaging (MRI) images. In particular, we specify the main rules defining the role of an operator and its relationship with other operators. Extensive experiments carried out on a wide dataset of MRI images of different body regions demonstrating usefulness and accuracy of the proposed approach are also reported.