Authors:
Jocival Dantas Dias Júnior
and
André R. Backes
Affiliation:
School of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, Uberlândia, MG, Brazil
Keyword(s):
Image Segmentation, Blood Cell, White Blood Cells, Leukocytes.
Abstract:
Blood smear image analysis is an essential task for many health related issues. Among the many blood structures present in these images, leukocytes play an important role in the detection of many diseases (such as leukemias), which can be detected by the amount, or abnormal aspect, of the leukocytes. To address this problem, this paper presents an unsupervised segmentation method for the nuclear structures in leukocytes. Our method uses color deconvolution to separate the dyes in different channels and a PSO algorithm to estimate an optimal kernel filter to combine local features in different stain channels to emphasize the leukocytes structures so that simple thresholding techniques are able to perform image segmentation. We also used a postprocessing approach based on morphological operators to refine the border of detected structures, thus improving our performance. We performed a comparison with different approaches found in literature using 367 images containing leukocytes and o
ther blood structures and results demonstrated the superiority of our approach in terms of Jaccard index.
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