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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 213901, 13 pages
Research Article

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

1Department of Life, Health and Environmental Sciences, University of L’Aquila, Via Vetoio Coppito 2, 67100 L’Aquila, Italy
2Department of Computer Science, Sapienza University of Rome, Via Salaria 113, 00198 Rome, Italy

Received 26 February 2013; Revised 1 May 2013; Accepted 8 May 2013

Academic Editor: Younghae Do

Copyright © 2013 Danilo Avola et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.