Automatic Segmentation and Classification of White Blood Cells in Peripheral Blood Samples

The present research was carried out in an attempt to identify the problems associated with the morphofunctional analysis of vertebrate animal’s leukocyte blood cells. For this blood samples of four age groups chickens (Gallus gallus L.) were collected and analysis was carried out as per the recommendations of the International Council for Standardization in Hematology for the identifying morpho-physiological characteristics of leukocytes of peripheral blood of birds. Results of the current study were based on the sample of early postnatal ontogenesis based on the analysis of high-resolution color microphotographs taken by the method of light-optical microscopy. Results of the current study revealed that the cells have the well-designed of all granular leukocytes typical "eosinophilic" nucleus with two segments. Sometimes cells contain polysegmental nuclei that are eccentrically located. Further, in contrast to heterophils, the eosinophilic nucleus has well-expressed contours, formed by irregularly shaped chromatin blocks with an optically denser structure. There are also eosinophils with stick-nuclei. The smallest blood granulocytes of the birds are basophils. Basophils differ in structure slightly basophilic color cytoplasm with intensively basophilic granules of rounded or other forms of different size. The sizes of granules in basophils are smaller than the other types of granular leukocytes. Thus, based on the analysis of qualitative color microphotographs of white blood cells of chickens of neonatal ontogenesis performed by optical microscopy, the differential morpho-physiological markers of leukocytic cells of peripheral blood of birds were marked and characterized.

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Isolation and two-step classification of normal white blood cells in peripheral blood smears

Journal of Pathology Informatics ◽

10.4103/2153-3539.93895 ◽

2012 ◽

Vol 3 (1) ◽

pp. 13 ◽

Cited By ~ 19

Author(s):

Nisha Ramesh ◽

MohammedE Salama ◽

Bryan Dangott ◽

Tolga Tasdizen

Keyword(s):

Peripheral Blood ◽

Blood Cells ◽

White Blood Cells ◽

Blood Smears ◽

Peripheral Blood Smears

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Classification of White Blood Cells Based on Cell Diameter, Specific Membrane Capacitance and Cytoplasmic Conductivity Leveraging Microfluidic Constriction Channel

2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) ◽

10.1109/transducers50396.2021.9495490 ◽

2021 ◽

Author(s):

Huiwen Tan ◽

Minruihong Wang ◽

Yi Zhang ◽

Xukun Huang ◽

Deyong Chen ◽

...

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Membrane Capacitance ◽

Cell Diameter ◽

Specific Membrane

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Adult Mice With Targeted Mutation of the Interleukin-11 Receptor (IL11Ra) Display Normal Hematopoiesis

Blood ◽

10.1182/blood.v90.6.2148 ◽

1997 ◽

Vol 90 (6) ◽

pp. 2148-2159 ◽

Cited By ~ 118

Author(s):

Harshal H. Nandurkar ◽

Lorraine Robb ◽

David Tarlinton ◽

Louise Barnett ◽

Frank Köntgen ◽

...

Keyword(s):

Bone Marrow ◽

Peripheral Blood ◽

Blood Cells ◽

Bone Marrow Cells ◽

White Blood Cells ◽

Null Mutation ◽

Wild Type ◽

Normal Numbers ◽

Interleukin 11 ◽

Marrow Cells

Abstract Interleukin-11 (IL-11) is a pleiotropic growth factor with a prominent effect on megakaryopoiesis and thrombopoiesis. The receptor for IL-11 is a heterodimer of the signal transduction unit gp130 and a specific receptor component, the α-chain (IL-11Rα). Two genes potentially encode the IL-11Rα: the IL11Ra and IL11Ra2 genes. The IL11Ra gene is widely expressed in hematopoietic and other organs, whereas the IL11Ra2 gene is restricted to only some strains of mice and its expression is confined to testis, lymph node, and thymus. To investigate the essential actions mediated by the IL-11Rα, we have generated mice with a null mutation of IL11Ra (IL11Ra−/−) by gene targeting. Analysis of IL11Ra expression by Northern blot and reverse transcriptase-polymerase chain reaction, as well as the absence of response of IL11Ra−/− bone marrow cells to IL-11 in hematopoietic assays, further confirmed the null mutation. Compensatory expression of the IL11Ra2 in bone marrow cells was not detected. IL11Ra−/− mice were healthy with normal numbers of peripheral blood white blood cells, hematocrit, and platelets. Bone marrow and spleen contained normal numbers of cells of all hematopoietic lineages, including megakaryocytes. Clonal cultures did not identify any perturbation of granulocyte-macrophage (GM), erythroid, or megakaryocyte progenitors. The number of day-12 colony-forming unit-spleen progenitors were similar in wild-type and IL11Ra−/− mice. The kinetics of recovery of peripheral blood white blood cells, platelets, and bone marrow GM progenitors after treatment with 5-flurouracil were the same in IL11Ra−/− and wild-type mice. Acute hemolytic stress was induced by phenylhydrazine and resulted in a 50% decrease in hematocrit. The recovery of hematocrit was comparable in IL11Ra−/− and wild-type mice. These observations indicate that IL-11 receptor signalling is dispensable for adult hematopoiesis.

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New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

10.1101/2021.04.29.441751 ◽

2021 ◽

Author(s):

Eslam Tavakoli ◽

Ali Ghaffari ◽

Seyedeh-Zahra Mousavi Kouzehkanan ◽

Reshad Hosseini

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Dice Similarity Coefficient ◽

Second Phase ◽

Peripheral Smear ◽

Color Features ◽

Svm Model ◽

Extraction Algorithm ◽

Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.49943 ◽

2019 ◽

Vol 9 (2) ◽

pp. 173

Author(s):

Apri Nur Liyantoko ◽

Ika Candradewi ◽

Agus Harjoko

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Back Propagation ◽

Cell Types ◽

Diagnostic Process ◽

The Subject ◽

Processing Techniques

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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