Author(s):
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. ; DAndreagiovanni, F. ; Divina, F. ; EsparciaAlcazar, A. L. ; DeVega, F. F. ; Glette, K. ; Hidalgo, J. I. ; Hubert, J. ; Iacca, G. ; Kramer, O. ; Mavrovouniotis, M. ; Garcia, AMM ; Nguyen, T. T. ; Schaefer, R. ; Silva, S. ; Tonda, A. ; Urquhart, N. ; Zhang, M.
Date: 2018
Persistent ID: http://hdl.handle.net/11449/164253
Origin: Oasisbr
Subject(s): Nuclear segmentation; Lymphoma histological images Genetic algorithm; Fitness function evaluation
Description
Made available in DSpace on 2018-11-26T17:51:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-01-01
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
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.
Fed Univ ABC, Ctr Math Comp & Cognit, Santo Andre, Brazil
Univ Fed Uberlandia, Inst Biomed Sci, Dept Histol & Morphol, Uberlandia, MG, Brazil
Sao Paulo State Univ, Dept Comp Sci & Stat, Sao Jose Do Rio Preto, Brazil
Univ Fed Uberlandia, Fac Comp Sci, Uberlandia, MG, Brazil
Sao Paulo State Univ, Dept Comp Sci & Stat, Sao Jose Do Rio Preto, Brazil
CAPES: 1575210
FAPEMIG: TEC - APQ-02885-15
