White Blood Cell Image Segmentation Based on Color Component Combination and Contour Fitting

2020 ◽  
Vol 15 (5) ◽  
pp. 463-471
Author(s):  
Chuansheng Wang ◽  
Hong Zhang ◽  
Zuoyong Li ◽  
Xiaogen Zhou ◽  
Yong Cheng ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
White Blood Cell ◽  
White Blood Cells ◽  
Color Component ◽  
Segmentation Accuracy ◽  
Contour Fitting ◽  
Canny Edge ◽  
Segmentation Task ◽  
Staining Condition

Background: White Blood Cell (WBC) image segmentation plays a key role in cell morphology analysis. However, WBC segmentation is still a challenging task due to the diversity of WBCs under different staining conditions. Objective: In this paper, we propose a novel WBC segmentation method based on color component combination and contour fitting to segment WBC images accurately. Methods: Specifically, the proposed method first uses color component combination and image thresholding to achieve nucleus segmentation, then uses a color prior to remove image background, and extracts the initial WBC contour via Canny edge detection, and finally judges and closes the unclosed WBC contour by contour fitting. Accordingly, cytoplasm segmentation is achieved by subtracting the nucleus region from the WBC region. Results: Experimental results on 100 WBC images under rapid staining condition and 50 WBC images under standard staining condition showed that the proposed method improved segmentation accuracy of white blood cells under rapid and standard staining conditions. Conclusion: The proposed color component combination and contour fitting is effective in WBC segmentation task.

scholarly journals White Blood Cells and Severe COVID-19: A Mendelian Randomization Study

2021 ◽  
Vol 11 (3) ◽  
pp. 195
Author(s):  
Yitang Sun ◽  
Jingqi Zhou ◽  
Kaixiong Ye
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
White Blood Cell Count ◽  
White Blood Cell ◽  
Blood Cells ◽  
Mendelian Randomization ◽  
Association Studies ◽  
White Blood Cells ◽  
Genome Wide Association Studies ◽  
Blood Cell Count

Increasing evidence shows that white blood cells are associated with the risk of coronavirus disease 2019 (COVID-19), but the direction and causality of this association are not clear. To evaluate the causal associations between various white blood cell traits and the COVID-19 susceptibility and severity, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses with summary statistics from the largest and most recent genome-wide association studies. Our MR results indicated causal protective effects of higher basophil count, basophil percentage of white blood cells, and myeloid white blood cell count on severe COVID-19, with odds ratios (OR) per standard deviation increment of 0.75 (95% CI: 0.60–0.95), 0.70 (95% CI: 0.54–0.92), and 0.85 (95% CI: 0.73–0.98), respectively. Neither COVID-19 severity nor susceptibility was associated with white blood cell traits in our reverse MR results. Genetically predicted high basophil count, basophil percentage of white blood cells, and myeloid white blood cell count are associated with a lower risk of developing severe COVID-19. Individuals with a lower genetic capacity for basophils are likely at risk, while enhancing the production of basophils may be an effective therapeutic strategy.

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%.

White Blood Cells Classification with Deep Convolutional Neural Networks

2018 ◽  
Vol 32 (09) ◽  
pp. 1857006 ◽  
Author(s):  
Ming Jiang ◽  
Liu Cheng ◽  
Feiwei Qin ◽  
Lian Du ◽  
Min Zhang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Network Architecture ◽  
White Blood Cells ◽  
Activation Function ◽  
Training Set ◽  
Deep Convolutional Neural Networks ◽  
Cell Image ◽  
Attending Physician

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate.

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

scholarly journals STUDIES ON THE PHYSIOLOGY OF THE WHITE BLOOD CELL

Blood ◽  
1947 ◽  
Vol 2 (3) ◽  
pp. 235-243 ◽  
Author(s):  
RICHARD WAGNER
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Glycogen Content ◽  
Striated Muscle ◽  
White Blood Cells ◽  
Glycogen Storage ◽  
Different Types ◽  
Blast Cells ◽  
Reducing Substances

Abstract The technic of determining glycogen in isolated white blood cells was applied to the study of the different types of leukemia and of polycythemia, in order to obtain information on the physiology of the white blood cell. From this study it is concluded that the granulated leukocyte is the only carrier of glycogen in whole blood. The "reducing substances" in lymphocytes and blast cells are not considered as true glycogen. The glycogen content of wet white blood cells in the rabbit amounts to about 1 per cent. In the human being a range of from 0.17 to 0.67 per cent was calculated. In disease higher percentages occur, in polycythemia up to 1.64 per cent and in glycogen storage disease up to 3.05 per cent. The glycogen concentration of normal white blood cells is within the same range as that of the striated muscle.

Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation

2012 ◽  
Vol 16 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Chastine Fatichah ◽  
◽  
Martin Leonard Tangel ◽  
Muhammad Rahmat Widyanto ◽  
Fangyan Dong ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Blood Cell ◽  
White Blood Cell ◽  
Cancer Diagnosis ◽  
Fuzzy Clustering ◽  
High Accuracy ◽  
Low Density ◽  
Fuzzy Morphology ◽  
Cell Image ◽  
Improve Accuracy

An Interest-based Ordering Scheme (IOS) for fuzzy morphology on White-Blood-Cell (WBC) image segmentation is proposed to improve accuracy of segmentation. The proposed method shows a high accuracy in segmenting both high- and low-density nuclei. Further, its running time is low, so it can be used for real applications. To evaluate the performance of the proposed method, 100 WBC images and 10 leukemia images are used, and the experimental results show that the proposed IOS segments a nucleus in WBC images 3.99% more accurately on average than the Lexicographical Ordering Scheme (LOS) does and 5.29% more accurately on average than the combined Fuzzy Clustering and Binary Morphology (FCBM) method does. The proposal method segments a cytoplasm 20.72% more accurately on average than the FCBM method. The WBC image segmentation is a part of WBC classification in an automatic cancer-diagnosis application that is being developed. In addition, the proposed method can be used to segment any images that focus on the important color of an object of interest.

scholarly journals Evaluation of total and differential white blood cell counts in workers exposed to lead

2022 ◽  
Author(s):  
F Kargar-Shouroki ◽  
HR Mehri ◽  
F Sepahi-Zoeram
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Lead Level ◽  
White Blood Cells ◽  
Blood Lead ◽  
Exposed Group ◽  
Toxic Heavy Metal ◽  
Cell Counts ◽  
Differential White Blood Cell ◽  
Battery Workers

Introduction: Lead is a toxic heavy metal that has adverse health effects on blood parameters. About 80% of lead produced is used in batteries, especially vehicle batteries. Therefore, the present study aimed to assess the hematological changes, including total and differential white blood cell (WBC) counts in battery workers exposed to lead, and compare with the non-exposed group. Materials and Methods: This cross-sectional study was carried out in a battery industry in Semnan city. The study population consisted of 78 battery workers and 78 healthy non-exposed office workers. A hematology cell counter was used to determine the total, and differential WBC counts. Blood lead level was measured in accordance with the NIOSH method 8003. Results: Blood lead levels were about two times higher than the TLV recommended by the American Conference of Governmental Industrial Hygienists (ACGIH) for this compound (20 µg/dl). The level of WBC (8.07± 2.55 mm3 blood×103 vs. 7.27 ± 1.58 mm3 blood×103) was significantly higher, while the level of monocyte was significantly lower (6.96 ± 1.72 % vs. 7.67 ± 1.87 %) in the exposed group than in the non-exposed group. After adjustment for potential confounders such as age and work history, a significant association between exposure to lead and WBC and monocyte levels was reported. Conclusion: The present study's findings indicated that exposure to lead was associated with total and differential white blood cells changes in the exposed group compared to the non-exposed group.

scholarly journals Correlation Between Ultrawide-Field Fluorescence Contrast Results and White Blood Cell Indexes in Diabetic Retinopathy

2021 ◽  
Author(s):  
Lili Huang ◽  
Lele Li ◽  
Min Wang ◽  
Dongmei Zhang ◽  
Yu Song
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Cell ◽  
White Blood Cell ◽  
Microvascular Complications ◽  
White Blood Cells ◽  
Diabetic Patients ◽  
Microvascular Leakage ◽  
Department Of Ophthalmology ◽  
Liver And Kidney ◽  
Fluorescence Contrast

Abstract Background: Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes. DR involves a state of systemic inflammation, and chronic inflammation can promote microvascular and macrovascular diseases in diabetic patients and accelerate disease progression. Ultrawide-field FFA (UWFA) systems are increasingly being used to examine a wider retina. To explore the correlation between the different manifestations of retinopathy under UWFA and the systemic indicators of white blood cells in patients with diabetic retinopathy .Methods: This retrospective study included the hospitalized DR patients in the Department of Ophthalmology and Endocrinology of the Affiliated Hospital 2 of Nantong University between January 2016 and March 2019. This study examined the correlations between the UWFA examination results and glycated hemoglobin (HbA1c), routine blood tests, blood coagulation function, liver and kidney function, and the neutrophil-to-lymphocyte ratio of patients with clinically diagnosed DR during hospitalization.Results: A total of 115 patients with DR (53 females and 62 males) were included (199 eyes: 102 right eyes and 97 left eyes). UWFA revealed that most eyes (77.4%) had grade 4 microvascular leakage, 52.8% had grade 0 capillary non-perfusion area, 59.3% had grade 0 neovascularization, and 92.0% had grade 0 fibrous proliferative membranes. Microvascular leakage was correlated with the NLR (r=0.186, P=0.027). Capillary non-perfusion area was correlated with the monocyte ratio (r=0.144, P=0.042) and the eosinophil ratio (r=0.123, P=0.044). Neovascularization was correlated to the monocyte ratio (r=0.324, P=0.018). Finally, the fibrous proliferative membrane was correlated to the monocyte ratio (r=0.418, P=0.002). Only the eosinophil ratio was independently associated with proliferative DR (odds ratio=1.25, 95% confidence interval: 1.04-1.51, P=0.018).Conclusion: The results of UWFA imaging in patients with DR are correlated with white blood cell population indexes. The eosinophil ratio was independently associated with proliferative DR.

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