Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images

Azevedo Tosta, Thaína A.; Faria, Paulo Rogério; Batista, Valério Ramos; Neves, Leandro Alves [UNESP]; do Nascimento, Marcelo Zanchetta

Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images

Arquivos

2-s2.0-85037999945.pdf (4.88 MB)

Data

2018-03-01

Autores

Azevedo Tosta, Thaína A.

Faria, Paulo Rogério

Batista, Valério Ramos

Neves, Leandro Alves

do Nascimento, Marcelo Zanchetta

Tipo

Artigo

Direito de acesso

Acesso aberto

Resumo

Histological images analysis is an important procedure to diagnose different types of cancer. One of them is the chronic lymphocytic leukemia (CLL), which can be identified by applying image segmentation techniques. This study presents an unsupervised method to segment neoplastic nuclei in CLL images. Firstly, deconvolution, histogram equalization and mean filter were applied to enhance nuclear regions. Then, a segmentation technique based on a combination of wavelet transform, fuzzy 2-partition entropy and genetic algorithm was used, followed by removal of false positive regions, and application of valley-emphasis and morphological operations. In order to evaluate the proposed algorithm H&E-stained histological images were used. In the accuracy metric, the proposed method attained more than 80%, which can surpass similar methods. This proposal presents spatial distribution that has a good consistency with a manual segmentation and lower overlapping rate than other techniques in the literature.