Open AccessA Polylobar Nucleus Identifying and Extracting Method for Leukocyte Counting
Author(s) -
Jin Chen,
Yiping Cao,
Jie Gao,
Haihua An
Publication year - 2021
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2021/5565156
Subject(s) - nucleus , segmentation , otsu's method , mitosis , pattern recognition (psychology) , identification (biology) , artificial intelligence , cell nucleus , computer science , biology , image segmentation , microbiology and biotechnology , botany
Accurate counting of leukocytes is an important method for diagnosing human blood diseases. Because most nuclei of neutrophils and eosinophils are polylobar, it is easily confused with the unilobar nuclei in nucleus segmentation. Therefore, it is very essential to accurately identify and determine the polylobar leukocytes. In this paper, a polylobar nucleus identification and extracting method is proposed. Firstly, by using the Otsu threshold and area threshold method, the nuclei of leukocytes are accurately segmented. According to the morphological characteristics of polylobar leukocytes, the edges of the mitotic polylobar leukocytes are detected, and the numbers of polylobar leukocytes are determined according to the minimal distance rule. Therefore, the accurate counting of leukocytes can be realized. From the experimental results, we can see that using the Otsu method and the area threshold to segment the polylobar nuclear leukocytes, the segmentation ratio of the leukocyte nucleus reached 98.3%. After using the morphological features, the polylobar nuclear leukocytes can be accurately counted. The experimental results have verified the feasibility and practicability of the proposed method.