Artif Intell Med - Local binary patterns variants as texture descriptors for medical image analysis.

Tópicos

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Resumo

JECTIVE: This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine).METHODS AND MATERIALS: Extensive experiments are conducted using the following three datasets:RESULTS AND CONCLUSION: Our results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets. EQP is based on an elliptic neighborhood and a 5 levels scale for encoding the local gray-scale difference. Particularly interesting are the results on the widely studied 2D-HeLa dataset, where, to the best of our knowledge, the proposed descriptor obtains the highest performance among all the several texture descriptors tested in the literature.

Resumo Limpo

jectiv paper focus use imagebas machin learn techniqu medic imag analysi particular present variant local binari pattern lbp wide consid state art among textur descriptor provid detail review literatur exist lbp variant discuss salient approach along pros con report new experi use sever lbpbase descriptor propos set novel textur descriptor represent biomed imag standard lbp oper defin grayscal invari textur measur deriv general definit textur local neighborhood variant obtain consid differ shape neighborhood calcul differ encod evalu local grayscal differ set featur use train machinelearn classifi standalon support vector machinemethod materi extens experi conduct use follow three datasetsresult conclus result show novel variant name elong quinari pattern eqp perform method among propos work extract inform textur test dataset eqp base ellipt neighborhood level scale encod local grayscal differ particular interest result wide studi dhela dataset best knowledg propos descriptor obtain highest perform among sever textur descriptor test literatur

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