Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm
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Data
2018-01-01
Autores
Tosta, Thaina A. A.
Faria, Paulo Rogerio de
Neves, Leandro Alves [UNESP]
Nascimento, Marcelo Zanchetta do
Sim, K.
Kaufmann, P.
Ascheid, G.
Bacardit, J.
Cagnoni, S.
Cotta, C.
Título da Revista
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Título de Volume
Editor
Springer
Resumo
For disease monitoring, grade definition and treatments orientation, specialists analyze tissue samples to identify structures of different types of cancer. However, manual analysis is a complex task due to its subjectivity. To help specialists in the identification of regions of interest, segmentation methods are used on histological images obtained by the digitization of tissue samples. Besides, features extracted from these specific regions allow for more objective diagnoses by using classification techniques. In this paper, fitness functions are analyzed for unsupervised segmentation and classification of chronic lymphocytic leukemia and follicular lymphoma images by the identification of their neoplastic cellular nuclei through the genetic algorithm. Qualitative and quantitative analyses allowed the definition of the Renyi entropy as the most adequate for this application. Images classification has reached results of 98.14% through accuracy metric by using this fitness function.
Descrição
Palavras-chave
Nuclear segmentation, Lymphoma histological images Genetic algorithm, Fitness function evaluation
Como citar
Applications Of Evolutionary Computation, Evoapplications 2018. Cham: Springer International Publishing Ag, v. 10784, p. 47-62, 2018.