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

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

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%.

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.

Optimized Binary Bat algorithm for classification of white blood cells

Measurement ◽  
2019 ◽  
Vol 143 ◽  
pp. 180-190 ◽  
Author(s):  
Deepak Gupta ◽  
Jatin Arora ◽  
Utkarsh Agrawal ◽  
Ashish Khanna ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Bat Algorithm

Automatic segmentation, counting, size determination and classification of white blood cells

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 58-65 ◽  
Author(s):  
Sedat Nazlibilek ◽  
Deniz Karacor ◽  
Tuncay Ercan ◽  
Murat Husnu Sazli ◽  
Osman Kalender ◽  
...  
Keyword(s):  
Blood Cells ◽  
Automatic Segmentation ◽  
White Blood Cells ◽  
Size Determination

scholarly journals Classification of Cells with Membrane Staining and/or Fixation Based on Cellular Specific Membrane Capacitance and Cytoplasm Conductivity

Micromachines ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Song-Bin Huang ◽  
Yang Zhao ◽  
Deyong Chen ◽  
Shing-Lun Liu ◽  
Yana Luo ◽  
...  
Keyword(s):  
Membrane Capacitance ◽  
Membrane Staining ◽  
Specific Membrane

ROBUST DISCRIMINATION OF LEUKOCYTES PROTUBERANT TYPES FOR EARLY DIAGNOSIS OF LEUKEMIA

2019 ◽  
Vol 19 (06) ◽  
pp. 1950055 ◽  
Author(s):  
ISRA NAZ ◽  
NAZEER MUHAMMAD ◽  
MUSSARAT YASMIN ◽  
MUHAMMAD SHARIF ◽  
JAMAL HUSSAIN SHAH ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Blood Cells ◽  
Detection System ◽  
White Blood Cells ◽  
Training Model ◽  
Blood Film ◽  
Morphological Examination ◽  
Efficient Detection ◽  
Laboratory Technology

Advanced laboratory technology has made blood testing more automated, robust and tests are being implemented comprehensively. Leukocytes type differentiation is a critical hematological images analysis step of blood film as it delivers valuable information in diagnosis of several diseases. At present, the morphological examination of leukocytes is done manually and this process is very tedious, inefficient and slow. Although many white blood cells detection or classification techniques are presented by different researchers, there is still a need of fully automated and an efficient detection system of blood cells with its particular types for an early diagnosis of leukemia. This paper presents a technique for the classification of protuberant types of leukocytes and early diagnosis of leukemia. The work is divided into the following main stages: (a) image augmentation, (b) wavelet composition and decomposition for attaining high and low frequency bands of the cell image, (c) convolutional neural network (CNN) training model for the classification of leukocytes categories and (d) prediction of leukemia. The main intention behind this study is to develop an automated, robust and efficient classification and detection system of leukocytes for microscopic blood images.

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