Lymphoma images analysis using morphological and non-morphological descriptors for classification

do Nascimento, Marcelo Zanchetta; Martins, Alessandro Santana; Azevedo Tosta, Thaína Aparecida; Neves, Leandro Alves [UNESP]

Lymphoma images analysis using morphological and non-morphological descriptors for classification

Arquivos

2-s2.0-85048075793.pdf (3.21 MB)

Data

2018-09-01

Autores

do Nascimento, Marcelo Zanchetta

Martins, Alessandro Santana

Azevedo Tosta, Thaína Aparecida

Neves, Leandro Alves

Tipo

Artigo

Direito de acesso

Acesso aberto

Resumo

Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.