Evaluation of statistical and Haralick texture features for lymphoma histological images classification

• Autor(a) principal: Azevedo Tosta, Thaína A.
• Data de Publicação: 2021
• Outros Autores: de Faria, Paulo R., Neves, Leandro A. [UNESP], do Nascimento, Marcelo Z.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1080/21681163.2021.1902401; http://hdl.handle.net/11449/206110
• topic: classification; Lymphoma histological images; nuclear segmentation; texture features; wavelet and ranklet transforms

The investigation of different types of cancer can be performed by images classification with features extracted from specific regions identified by a segmentation step. Therefore, this study presents the evaluation of texture features extracted from neoplastic nuclei for the classification of lymphomas images. The neoplastic nuclei were segmented by steps of pre and post-processing and a thresholding. Statistical and Haralick’s features extracted from wavelet and ranklet transforms were evaluated with different classifiers. The use of the statistical metrics from the wavelet transform in association with the K-nearest neighbour classifier provided the best results in most of the two-class classifications.