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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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The Nucleus

The Nucleus

The nucleus is a membrane-bound organelle that acts as a control center in a eukaryotic cell. It contains chromosomal DNA, which controls gene expression and precisely regulates the production of proteins within the cell. In contrast, the DNA inside the mitochondria and chloroplast only carries out functions that are specific to those organelles.
Arrangement of DNA within Nucleus
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Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

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Leukocyte nucleus segmentation and nucleus lobe counting.

Yung-Kuan Chan¹, Meng-Hsiun Tsai, Der-Chen Huang

¹Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.

BMC Bioinformatics

|November 16, 2010

Summary

An automated system for leukocyte nuclei segmentation and lobe counting improves diagnostic accuracy for conditions like B12 deficiency. This method offers better accuracy than manual analysis, aiding in disease detection.

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Area of Science:

Hematology
Medical Image Analysis
Computational Biology

Background:

Leukocytes are crucial for the immune system, with variations in their count and morphology indicating various health conditions.
Abnormal leukocyte morphology, such as neutrophil hypersegmentation, is a key indicator for deficiencies like Vitamin B12 or folate.
Current manual identification of abnormal leukocytes by biomedical technologists has limitations in quality and efficiency.

Purpose of the Study:

To develop an automatic leukocyte nuclei image segmentation method for enhanced recognition technology.
To create a method for accurately counting the number of lobes within a leukocyte cell nucleus.

Main Methods:

Development of a Leukocyte Nuclei Segmentation (LNS) method to isolate nuclei from blood smear images.
Implementation of a Lobe Counting (LC) method for splitting leukocyte nuclei into lobes.
Utilized advanced image processing techniques: weighted Sobel operator, Gradient Direction Weight (GDW), and Genetic-based Parameter Detector (GBPD).

Main Results:

The LNS method demonstrated high accuracy in segmenting leukocyte nuclei, proving insensitive to image variations.
The LC method successfully split leukocyte nuclei into lobes, contributing to accurate morphological analysis.
Experimental results showed impressive segmentation accuracy and expressive performance for both developed methods.

Conclusions:

The LNS and LC methods offer a robust and efficient approach to analyzing leukocyte nuclei from blood smear images.
These automated methods provide superior performance metrics (UR, ER, RDE) compared to traditional techniques.
The proposed image processing techniques enhance the capabilities of automated leukocyte analysis for clinical diagnostics.