scholarly journals Membrane capacitance of thousands of single white blood cells

2017 ◽  
Vol 14 (137) ◽  
pp. 20170717 ◽  
Author(s):  
Ke Wang ◽  
Chun-Chieh Chang ◽  
Tzu-Keng Chiu ◽  
Xiaoting Zhao ◽  
Deyong Chen ◽  
...  
Keyword(s):  
Blood Cells ◽  
Single Cells ◽  
Statistical Significance ◽  
Gradient Centrifugation ◽  
White Blood Cells ◽  
Membrane Capacitance ◽  
Label Free ◽  
Specific Membrane ◽  
Total Membrane

As label-free biomarkers, the electrical properties of single cells are widely used for cell type classification and cellular status evaluation. However, as intrinsic cellular electrical markers, previously reported membrane capacitances (e.g. specific membrane capacitance C spec and total membrane capacitance C mem ) of white blood cells were derived from tens of single cells, lacking statistical significance due to low cell numbers. In this study, white blood cells were first separated into granulocytes and lymphocytes by density gradient centrifugation and were then aspirated through a microfluidic constriction channel to characterize both C spec and C mem . Thousands of granulocytes ( n cell = 3327) and lymphocytes ( n cell = 3302) from 10 healthy blood donors were characterized, resulting in C spec values of 1.95 ± 0.22 µF cm −2 versus 2.39 ± 0.39 µF cm −2 and C mem values of 6.81 ± 1.09 pF versus 4.63 ± 0.57 pF. Statistically significant differences between granulocytes and lymphocytes were located for both C spec and C mem . In addition, neural network-based pattern recognition was used to classify white blood cells, producing successful classification rates of 78.1% for C spec and 91.3% for C mem , respectively. These results indicate that as intrinsic bioelectrical markers, membrane capacitances may contribute to the classification of white blood cells.

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

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.

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

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%.

Classification of blood cells and tumor cells using label-free ultrasound and photoacoustics

2015 ◽  
Vol 87 (8) ◽  
pp. 741-749 ◽  
Author(s):  
Eric M. Strohm ◽  
Michael C. Kolios
Keyword(s):  
Tumor Cells ◽  
Blood Cells ◽  
Label Free

scholarly journals How Similar are Pharmacy Students and White Blood Cells? Curricular Content Reinforced Through Personification

2019 ◽  
Vol 10 (3) ◽  
pp. 12
Author(s):  
Kenric Ware
Keyword(s):  
Blood Cells ◽  
Statistical Significance ◽  
White Blood Cells ◽  
Pharmacy Education ◽  
Pharmacy Students ◽  
First Year ◽  
Roles And Responsibilities ◽  
Curricular Content ◽  
Article Type ◽  
And Gender

Description: This initiative sought to evaluate the use of personification to reinforce immunology concepts among pharmacy students.  A two-part question posed to first year pharmacy students asked if they could physically become two white blood cells (WBCs), which would they choose and why. Students received instruction in immunology prior to providing their feedback. Demographics included campus of enrollment and gender designation. Student ratings 1 to 5 reflected approval levels toward this activity’s usefulness (1: least; 5 most). Key Findings: One hundred and ten of 117 students selected two WBCs they would physically become if possible (94%). Less than two-thirds of students were female (63%) and the Columbia campus featured approximately a quarter of the students (24%). The most and least common WBCs chosen, as first selections by campus and gender, were statistically significant being neutrophils and basophils, lymphocytes and eosinophils, respectively. The median approval values of the WBC personification activity by campus and gender were similar and did not reach statistical significance, 4.5 and 5, respectively. Conclusion: Pharmacy students commended the personification activity for helping them learn the roles and responsibilities of WBCs. Unique and insightful rationales for the choices made for WBCs persisted among the students. In light of these favorable reviews, this type of activity can be adapted to other areas of pharmacy education.   Article Type: Note

Segmentation of Leukemia Cells Using Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 39-48
Author(s):  
Eman Mostafa ◽  
Heba A. Tag El-Dien
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Microscopic Image ◽  
Processing Stage ◽  
Clustering Techniques ◽  
Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

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