BackgroundLeukocytes play an important role in the human immune system. The family of leukocytes is comprised of lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Any infection or acute stress may increase or decrease the number of leukocytes. An increased percentage of neutrophils may be caused by an acute infection, while an increased percentage of lymphocytes can be caused by a chronic bacterial infection. It is important to realize an abnormal variation in the leukocytes. The five types of leukocytes can be distinguished by their cytoplasmic granules, staining properties of the granules, size of cell, the proportion of the nuclear to the cytoplasmic material, and the type of nucleolar lobes. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.Biomedical technologists can currently recognize abnormal leukocytes using human eyes. However, the quality and efficiency of diagnosis may be compromised due to the limitations of the biomedical technologists' eyesight, strength, and medical knowledge. Therefore, the development of an automatic leukocyte recognition system is feasible and necessary. It is essential to extract the leukocyte region from a blood smear image in order to develop an automatic leukocyte recognition system. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.ResultsThe purpose of this paper is to contribute an automatic leukocyte nuclei image segmentation method for such recognition technology. The other goal of this paper is to develop the method of counting the number of lobes in a cell nucleus. The experimental results demonstrated impressive segmentation accuracy.ConclusionsInsensitive to the variance of images, the LNS (Leukocyte Nuclei Segmentation) method functioned well to isolate the leukocyte nuclei from a blood smear image with much better UR (Under Segmentation Rate), ER (Overall Error Rate), and RDE (Relative Distance Error). The presented LC (Lobe Counting) method is capable of splitting leukocyte nuclei into lobes. The experimental results illuminated that both methods can give expressive performances. In addition, three advanced image processing techniques were proposed as weighted Sobel operator, GDW (Gradient Direction Weight), and GBPD (Genetic-based Parameter Detector).
In this paper, a leukocyte segmentation and recognition method is proposed for leukocyte differential counting. In general, leukocytes are usually manually classified in laboratories by using microscopes. It is a painstaking and subjective task for biologists. An automatic method is essential to reduce the overhead for biologists. The nuclei are used to identify five types of leukocyte in this paper. The leukocyte cell nucleus enhancer is proposed to segment the region we are interested in by enhancing the region of the leukocyte nucleus and suppressing the other region of the blood smear images. In the recognition steps, we reduce features by principle component analysis (PCA) to obtain suitable features. The genetic algorithm based kmeans clustering approach is used to classify the five kinds of leukocyte in the reduced dimensions. The experimental results show that even though only leukocyte nucleus features are used for classification in our method, we achieve a high and promised accurate recognition rate.
Leukocyte segmentation and location detection is the most important preprocessing step for further recognition. In this paper, an adaptive leukocyte segmentation method is proposed. Two kinds of color spaces are considered to enhance the nuclei. With the combined color space, the variety of stain and light condition can be avoided. In order to deal with different sizes of images, an adaptive segmentation method based on genetic algorithm is proposed. The experimental result shows that we can obtain promised segmental results even apply on different color tone and size of smear images.
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