Comput Math Methods Med - Customized first and second order statistics based operators to support advanced texture analysis of MRI images.

Tópicos

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Resumo

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.

Resumo Limpo

textur analysi process highlight key characterist thus provid exhaust unambigu mathemat descript object repres digit imag characterist connect specif properti object case mention properti repres aspect visual percept can detect develop oper base comput vision techniqu case properti visual percept comput obtain develop oper base imag understand approach pixel compos high qualiti medic imag can consid result stochast process sinc repres morpholog physiolog process empir observ shown imag visual percept hidden signific aspect reason oper can develop mean statist approach paper present set custom first second order statist base oper perform advanc textur analysi magnet reson imag mri imag particular specifi main rule defin role oper relationship oper extens experi carri wide dataset mri imag differ bodi region demonstr use accuraci propos approach also report

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