Author(s):
Tosta, Thaina Aparecida Azevedo ; De Abreu, Andressa Finzi ; Travencolo, Bruno Augusto Nassif ; Do Nascimento, Marcelo Zanchetta D. ; Neves, Leandro Alves [UNESP]
Date: 2018
Persistent ID: http://hdl.handle.net/11449/177542
Origin: Oasisbr
Subject(s): Blood Smear Images; Deconvolution; Leukocytes; Nucleus; Segmentation; Thresholding; White Blood Cells; Blood Smear Images; Blood Smear Images; Deconvolution; Deconvolution; Leukocytes; Leukocytes; Nucleus; Nucleus; Segmentation; Segmentation; Thresholding; Thresholding; White Blood Cells; White Blood Cells
Description
Made available in DSpace on 2018-12-11T17:25:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-01-01
Blood smear image analysis is essential to correlate the amount of leukocytes in these images with malignancies such as the leukemias. Techniques of digital image processing can be used to aid pathologists in this analysis, leading to appropriate treatments for the patient. This paper presents an unsupervised segmentation method for the nuclear structures in leukocytes. Deconvolution was used to split the Giemsa stain components and the regions of interest were selected using a thresholding algorithm called Neighborhood Valley-emphasis. A postprocessing approach based on morphological operators was applied in these detected structures. The proposed algorithm was tested on 367 images containing leukocytes and other blood structures. A performance analysis was conducted through the Jaccard and accuracy metrics featuring results of 89.89% and 99.57%, respectively. Such results were compared to other published articles and this was considered the most promising method.
Department of Computer Science, Federal University of Uberlândia, UFU
Department of Computer Science and Statistics, São Paulo State University, UNESP
Department of Computer Science and Statistics, São Paulo State University, UNESP
