scholarly journals Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

Scanning ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fatih Veysel Nurçin ◽  
Elbrus Imanov
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Human Error ◽  
Local Minimum ◽  
Machine Learning Algorithms ◽  
Watershed Segmentation ◽  
Hole Filling ◽  
Novel Approach ◽  
Manual Counting ◽  
Background Segmentation

Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.

scholarly journals Classification of Red Blood Cells using Principal Component Analysis Technique

2019 ◽  
Vol 4 (2) ◽  
pp. 17-22 ◽  
Author(s):  
Jameela Ali Alkrimi ◽  
Sherna Aziz Tome ◽  
Loay E. George
Keyword(s):  
Principal Component Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Novel Approach ◽  
Analysis Technique

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

scholarly journals Centrifugation-free washing: A novel approach for removing immunoglobulin A from stored red blood cells

2018 ◽  
Vol 93 (4) ◽  
pp. 518-526 ◽  
Author(s):  
Eszter Vörös ◽  
Nathaniel Z. Piety ◽  
Briony C. Strachan ◽  
Madeleine Lu ◽  
Sergey S. Shevkoplyas
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Immunoglobulin A ◽  
Novel Approach

scholarly journals A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6221
Author(s):  
Rahman Shafique ◽  
Hafeez-Ur-Rehman Siddiqui ◽  
Furqan Rustam ◽  
Saleem Ullah ◽  
Muhammad Abubakar Siddique ◽  
...  
Keyword(s):  
Human Error ◽  
Acoustic Analysis ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Railway Track ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Traditional Procedure ◽  
Novel Approach ◽  
Tree Classifier

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

scholarly journals Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2455
Author(s):  
Han-Sang Park ◽  
Hillel Price ◽  
Silvia Ceballos ◽  
Jen-Tsan Chi ◽  
Adam Wax
Keyword(s):  
Red Blood Cells ◽  
High Throughput ◽  
Blood Cells ◽  
Storage Time ◽  
Single Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Microfluidic Channels ◽  
Phase Imaging ◽  
Time Points

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.

scholarly journals Red Blood Cells-Coupled tPA Prevents Impairment of Cerebral Vasodilatory Responses and Tissue Injury in Pediatric Cerebral Hypoxia/Ischemia through Inhibition of ERK MAPK Activation

2009 ◽  
Vol 29 (8) ◽  
pp. 1463-1474 ◽  
Author(s):  
William M Armstead ◽  
Kumkum Ganguly ◽  
John W Kiessling ◽  
Xiao-Han Chen ◽  
Douglas H Smith ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Neuronal Degeneration ◽  
Tissue Injury ◽  
Mapk Pathway ◽  
Fibrinolytic Therapy ◽  
Mitogen Activated Protein Kinase ◽  
Tissue Type ◽  
Erk Mapk ◽  
Novel Approach

Babies experience hypoxia (H) and ischemia (I) from stroke. The only approved treatment for stroke is fibrinolytic therapy with tissue-type plasminogen activator (tPA). However, tPA potentiates H/I-induced impairment of responses to cerebrovasodilators such as hypercapnia and hypotension, and blockade of tPA-mediated vasoactivity prevents this deleterious effect. Coupling of tPA to red blood cells (RBCs) reduces its central nervous system (CNS) toxicity through spatially confining the drug to the vasculature. Mitogen-activated protein kinase (MAPK), a family of at least three kinases, is upregulated after H/I. In this study we determined whether RBC-tPA given before or after cerebral H/I would preserve responses to cerebrovasodilators and prevent neuronal injury mediated through the extracellular signal-related kinase (ERK) MAPK pathway. Animals given RBC-tPA maintained responses to cerebrovasodilators at levels equivalent to pre-H/I values. cerebrospinal fluid and brain parenchymal ERK MAPK was elevated by H/I and this upregulation was potentiated by tPA, but blunted by RBC-tPA. U0126, an ERK MAPK antagonist, also maintained cerebrovasodilation post H/I. Neuronal degeneration in CA1 hippocampus after H/I was not improved by tPA, but was ameliorated by RBC-tPA and U0126. These data suggest that coupling of tPA to RBCs offers a novel approach toward increasing the benefit/risk ratio of thrombolytic therapy for CNS disorders associated with H/I.

scholarly journals PERFORMANCE EVALUATION OF NUCLEATED RED BLOOD CELL (NRBC) COUNT USING A FULLY AUTOMATED HAEMATOLOGY ANALYZER VERSUS MANUAL COUNTING

2021 ◽  
Vol 71 (5) ◽  
pp. 1806-10
Author(s):  
Tanweer Ahmed ◽  
Asad Mahmood ◽  
Nasir Uddin ◽  
Helen Mary Robert ◽  
Muhammad Ashraf ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Linear Regression Analysis ◽  
Cell Counting ◽  
Nucleated Red Blood Cells ◽  
Manual Counting ◽  
Blood Cell Counting ◽  
Nucleated Red Blood Cell

Objective: To evaluate the performance of Nucleated RBC (NRBC) Count using a fully automated haematology analyzer versus manual counting. Study Design: Cross-Sectional Study. Place and Duration of Study: Department of Hematology, Armed Forces Institute of Pathology, from Sep 2019-Jun 2020. Methodology: Routine fresh whole blood samples were run on Sysmex XN-3000 automated haematology analyzer and 384 samples with results of ≥0.1% Nucleated red blood cells were included in this study. Manual NRBC counting was carried out twice on Leishman-stained peripheral blood smears from all 384 samples. Comparison between manual and automated nucleated red blood cell counting methods was statistically analyzed through linear regression analysis & coefficient correlation. The degree of agreement between two methods was analyzed through Bland-Altman plot. Finally, concordance between the two methods was also analyzed at 5 different ranges of nucleated red blood cells. Results: Linear regression analysis revealed a (r2) value of 0.97. Regression equation was calculated as XN = 0.76MC ± 1.28, with 95% limits of agreement between ± 40.42% and -24.47%. A mean bias of 7.97% was demonstrated through Bland-Altman plot. Concordance analysis revealed a concordance rate of 93.74% (360/384). Nucleated red blood cell counting between two methods were more concordant when nucleated red blood cell counts were <200%. Conclusion: Nucleated red blood cells counting by XN-3000 automated hematology analyzer is statistically comparable to manual nucleated red blood cell counting. We suggest that automated counting can be adopted in routine hematology laboratory as a replacement of manual NRBC counting.

scholarly journals Characterizing stored red blood cells using ultra-high throughput holographic cytometry

2021 ◽  
Author(s):  
Han Sang Park ◽  
Hillel Price ◽  
Silvia Ceballos ◽  
Jen-Tsan Chi ◽  
Adam Wax
Keyword(s):  
Red Blood Cells ◽  
High Throughput ◽  
Blood Cells ◽  
Storage Time ◽  
Morphological Changes ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Phase Image ◽  
Microfluidic Channels ◽  
Imaging Data

AbstractHolographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase image based on off-axis interferometry of cells flowing through parallel microfluidic channels. Here, it is applied for characterizing morphological changes of red blood cells during storage under regular blood bank condition. The approach allows high quality phase imaging of a large number of cells greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology measurements show multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine learning algorithms. For the study here, logistic regression is used to classify the cells according to the storage time points. The results of the classifiers demonstrate the potential of holographic cytometry as a diagnostic tool.

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