Classification of breast and colorectal tumors based on percolation of color normalized images
| Autor: | Marcelo Zanchetta do Nascimento, Thaina Aparecida Azevedo Tosta, Leandro Alves Neves, Alessandro Santana Martins, Paulo Rogério de Faria, Guilherme Freire Roberto |
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| Přispěvatelé: | Universidade Federal de Uberlândia (UFU), Federal Institute of Triângulo Mineiro (IFTM), Universidade Federal do ABC (UFABC), Universidade Estadual Paulista (Unesp) |
| Rok vydání: | 2019 |
| Předmět: |
Color normalization
Image classification business.industry Computer science General Engineering Percolation 020207 software engineering Pattern recognition 02 engineering and technology Colorectal tumors Computer Graphics and Computer-Aided Design Human-Computer Interaction Fractal Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Breast tumors Colorectal tumor Colorectal Tumors |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| ISSN: | 0097-8493 |
| Popis: | Made available in DSpace on 2020-12-12T01:39:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Percolation is a fractal descriptor that has been applied recently on computer vision problems. We applied this descriptor on 58 colored histological breast images, and 165 colored histological colorectal images, both stained with Hematoxylin and Eosin, in order to extract features to differentiate between benign and malignant cases. The experiments were also performed over normalized images, aiming to analyze the influence of different color normalization techniques on percolation-based features and whether they can provide better classification results. The feature sets obtained from the application of the method on the original images and on the normalized images with three different techniques were tested using 12 different classifiers. We compared the obtained results with other relevant methods in the area and observed significant contributions, with AUC rates above 0.900 in both normalized and non-normalized images. We also verified that color normalization does not contribute to the classification of breast tumors when associated with percolation features. However, color normalized images from the colorectal tumor's dataset provided better results than the original images. Faculty of Computation (FACOM) Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N Center of Mathematics Computing and Cognition Federal University of ABC (UFABC), Av. dos Estados, 5001 Department of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265 Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265 CNPq: #304848/2018-2 CNPq: #313365/2018-0 CNPq: #427114/2016-0 CNPq: #430965/2018-4 FAPEMIG: #APQ-00578-18 CAPES: 001 |
| Databáze: | OpenAIRE |
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