Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic
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Data
2015-01-01
Autores
Pavarino, Eduardo [UNESP]
Neves, Leandro Alves [UNESP]
Nascimento, Marcelo Zanchetta do [UNESP]
Godoy, Moacir Fernandes de
Arruda, Pedro Francisco de
Santi Neto, Dalísio de
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Iop Publishing Ltd
Resumo
In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.
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3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014). Bristol: Iop Publishing Ltd, v. 574, p. 1-4, 2015.