期刊
COMPUTERS & GRAPHICS-UK
卷 84, 期 -, 页码 134-143出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cag.2019.08.008
关键词
Percolation; Color normalization; Image classification; Colorectal tumors; Breast tumors
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) [001]
- National Council for Scientific and Technological Development CNPq [4271142016-0, 304848/20182, 430965/2018-4, 313365/2018-0]
- State of Minas Gerais Research Foundation -FAPEMIG [APQ-00578-18]
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. (C) 2019 Published by Elsevier Ltd.
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