Automatic Method to Classify Images Based on Multiscale Fractal Descriptors and Paraconsistent Logic

3rd International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Pavarino, E.^1 ; Neves, L.A.^1 ; Nascimento, M.Z.^1 ; Godoy, M.F.^2,3 ; Arruda, P.F.^3 ; Neto, D.S.^4

São Paulo State University (UNESP), Department of Computer Science and Statistics (DCCE), Brazil^1

FAMERP-São José Do Rio Preto, Brazil^2

Transdisciplinary Center for Study of Chaos and Complexity (NUTECC), Brazil^3

Hospital de Base de São José Do Rio Preto, Department of Pathology, Brazil^4

关键词: Accuracy level;    Automatic method;    Multi-scale approaches;    Multiscale fractals;    Paraconsistent logic;    Prostate cancers;    Reference patterns;    Regions of interest;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/pdf
DOI  :  10.1088/1742-6596/574/1/012135

来源: IOP

【 摘 要 】

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 Best- first Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.