White Blood Cells Segmentation and Classification Using Swarm Optimization Algorithms and Multilayer Perceptron

2021 ◽  
Vol 13 (2) ◽  
pp. 16-30
Author(s):  
Shahd Tarek ◽  
Hala M. Ebied ◽  
Aboul Ella Hassanien ◽  
Mohamed F. Tolba
Keyword(s):  
Multilayer Perceptron ◽  
Blood Cells ◽  
White Blood Cells ◽  
Threshold Value ◽  
Leukemia Cells ◽  
Second Phase ◽  
Gray Wolf ◽  
Data Sets ◽  
Classification Problems ◽  
Third Phase

This study proposes a segmentation and classification system for early detection of blood disease; the proposed system consists of three phases. The first phase is segmenting white blood cells using multi-level thresholding optimized by the butterfly optimization algorithm to select the optimal threshold value to increase the accuracy. The second phase is extracting geometric and shape features of the segmented cells. The third phase is using the gray wolf optimizer to adopt the weights and biases of the multilayer perceptron to enhance the accuracy of classification between normal and leukemia cells, classify the normal cells to their five categories, and classify the leukemia to their four categories. The proposed system applies to different data sets (ALL-IDB2, LISC, and ASH-Image bank) and overcomes the segmentation and classification problems of microscopic images and shows an outstanding segmentation result, 98.02%; and the average classification accuracy between normal and leukemia cells is 98.58%, between white blood cell categories is 98.9%, and between leukemia types is 98.93%.

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.

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.

Targeting Survivin Using ICG-001 May Overcome Drug Resistance in Primary B-Cell Acute Lymphoblastic Leukemia.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3072-3072
Author(s):  
Enzi Jiang ◽  
Eugene Park ◽  
Carlton Scharman ◽  
Yao-Te Hsieh ◽  
Asha Kadavallore ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Control Group ◽  
Drug Resistant ◽  
Control Groups ◽  
Standard Chemotherapy

Abstract Abstract 3072 Poster Board III-9 Despite advances in chemotherapeutic treatment of acute lymphoblastic leukemia (ALL), 20% of children relapse with high death rates, highlighting the need for new treatment modalities. Recent population studies have demonstrated that Survivin, a member of the inhibitor of apoptosis (IAP) family proteins, is expressed in most cancerous cells but has also been implicated in normal erythropoiesis. It is upregulated in ALL of relapsed patients but not in drug-sensitive ALL. The expression of Survivin depends on the formation of a complex between β-catenin and its co-activator CBP. Selective suppression of CBP/β-catenin signaling using the novel small-molecule inhibitor ICG-001 offers a novel mechanism to target Survivin in the sensitization of leukemia cells to conventional drug treatment. We hypothesize that inhibition of CBP/β-catenin signaling by ICG-001 in combination with conventional therapy represents a promising therapeutic principle to eradicate drug resistant ALL while sparing normal hematopoiesis. An in vivo study utilized our bioluminescent model to non-invasively monitor leukemogenesis of a primary ALL, transduced with a lentiviral construct encoding firefly luciferase prior to xenotransplantation. NOD/SCIDIL2R gamma-/- mice were sublethally irradiated prior intravenous injection of 50,000 cells per animal. Leukemic animals were treated with a combination of intraperitoneally administered VDL and ICG-001 (100mg/kg/d) (n=3), which was delivered via subcutaneous osmotic pumps to ensure stable plasma levels, with VDL only (n=4), or PBS only (n=2) as a control for 4 weeks. Bioluminescent imaging on Day 42 post-injection showed a contrast in the containment of leukemia of ICG-001+VDL mice as compared to those of the VDL control group. The animals in the PBS control group and the VDL+PBS Pump control groups had Median Survival Times (MST) of 35 days and 66.5 days post-treatment, respectively. In marked contrast, the animals treated with a combination of VDL+ICG-001 had a significant 14% extension in MST of 76 days post-treatment (p=0.016 compared to VDL group). Survivin mRNA expression was found to be downregulated after VDL+ICG treatment compared to treatment with VDL only. Analysis of peripheral blood showed no effect of ICG-001 on leukocyte or red blood cells compared to control groups. Next, we determined in vitro the ability of ICG-001 to increase sensitivity of patient-derived ALL cells and ALL celllines including BEL-1, REH, 697 and SUPB15 to chemotherapy including VDL or Imatinib. After 4 days we observed significantly increased toxicity assessed by MTT assay and AnnexinV staining as well as downregulation of Survivin confirmed by real-time PCR and Western Blot. To determine if ICG-001 is non-toxic to normal hematopoiesis, we treated normalC57BL/6 mice for 3 weeks with ICG-001 only. At end of treatment, normal blood counts including red blood cell, white blood cells and platelets, normal histology and normal weight gain indicated that ICG-001 is not detrimental to the recipient. In vitro apoptotic studies using normal white blood cells isolated from peripheral blood and co-cultured with a stromal layer confirmed further the non-toxicity of ICG-001 to normal cells. In summary, the sustained survival of the mice treated with combination of standard chemotherapy and ICG-001 is compatible with our hypothesis that ICG-001 can sensitize drug resistant leukemia cells to treatment with standard chemotherapy while sparing normal hematopoiesis and may lead to novel therapeutic options to overcome drug resistance. Disclosures No relevant conflicts of interest to declare.

scholarly journals Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ahmet Çınar ◽  
Seda Arslan Tuncer
Keyword(s):  
Blood Cells ◽  
Feature Vector ◽  
White Blood Cells ◽  
Structural Features ◽  
The Body ◽  
Diagnostic Process ◽  
Support Vector ◽  
Data Sets ◽  
Svm Model

AbstractWhite blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.

Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression

2006 ◽  
Vol 18 (7) ◽  
pp. 1678-1710 ◽  
Author(s):  
D. L. Shrestha ◽  
D. P. Solomatine
Keyword(s):  
Estimation Error ◽  
Threshold Value ◽  
Data Sets ◽  
Classification Problems ◽  
M5 Model Tree ◽  
Model Tree ◽  
Regression Problems ◽  
Learning Machine ◽  
Boosting Technique ◽  
M5 Model

The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.

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

2021 ◽  
Author(s):  
Eslam Tavakoli ◽  
Ali Ghaffari ◽  
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

Abstract This article addresses a new method for the 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 shapes 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 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the 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 New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sajad Tavakoli ◽  
Ali Ghaffari ◽  
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

AbstractThis article addresses a new method for the 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 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 shapes 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 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.

scholarly journals Application Dependent End-of-Life Threshold Definition Methodology for Batteries in Electric Vehicles

Batteries ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 12
Author(s):  
Mikel Arrinda ◽  
Mikel Oyarbide ◽  
Haritz Macicior ◽  
Eñaut Muxika ◽  
Hartmut Popp ◽  
...  
Keyword(s):  
End Of Life ◽  
Electric Vehicle ◽  
Equivalent Circuit Model ◽  
Health Indicators ◽  
Threshold Value ◽  
High Energy ◽  
Second Phase ◽  
Life Event ◽  
Third Phase ◽  
Fixed End

The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations.

Ultrastructural changes in murine lymphoma cells exposed to ultraviolet-A radiation

1991 ◽  
Vol 49 ◽  
pp. 104-105
Author(s):  
Delma P. Thomas ◽  
Dianne E. Godar
Keyword(s):  
Medical Devices ◽  
Blood Cells ◽  
White Blood Cells ◽  
Consumer Products ◽  
Ultrastructural Changes ◽  
Ultraviolet A ◽  
Uv Spectrum ◽  
Lymphoma Cells ◽  
Murine Lymphoma ◽  
Transmission Electron

Ultraviolet radiation (UVR) from all three waveband regions of the UV spectrum, UVA (320-400 nm), UVB (290-320 nm), and UVC (200-290 nm), can be emitted by some medical devices and consumer products. Sunlamps can expose the blood to a considerable amount of UVR, particularly UVA and/or UVB. The percent transmission of each waveband through the epidermis to the dermis, which contains blood, increases in the order of increasing wavelength: UVC (10%) < UVB (20%) < UVA (30%). To investigate the effects of UVR on white blood cells, we chose transmission electron microscopy to examine the ultrastructure changes in L5178Y-R murine lymphoma cells.

The Effect of Red Blood Cells on the ADP-induced Aggregation of Human Platelets in Flow Through Tubes

1990 ◽  
Vol 63 (01) ◽  
pp. 112-121 ◽  
Author(s):  
David N Bell ◽  
Samira Spain ◽  
Harry L Goldsmith
Keyword(s):  
Platelet Aggregation ◽  
Shear Rate ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Platelet Rich Plasma ◽  
Mean Transit Time ◽  
White Blood Cells ◽  
Aggregate Size ◽  
Human Platelets ◽  
Induced Aggregation

SummaryThe effect of red blood cells, rbc, and shear rate on the ADPinduced aggregation of platelets in whole blood, WB, flowing through polyethylene tubing was studied using a previously described technique (1). Effluent WB was collected into 0.5% glutaraldehyde and the red blood cells removed by centrifugation through Percoll. At 23°C the rate of single platelet aggregtion was upt to 9× greater in WB than previously found in platelet-rich plasma (2) at mean tube shear rates Ḡ = 41.9,335, and 1,920 s−1, and at both 0.2 and 1.0 µM ADP. At 0.2 pM ADP, the rate of aggregation was greatest at Ḡ = 41.9 s−1 over the first 1.7 s mean transit time through the flow tube, t, but decreased steadily with time. At Ḡ ≥335 s−1 the rate of aggregation increased between t = 1.7 and 8.6 s; however, aggregate size decreased with increasing shear rate. At 1.0 µM ADP, the initial rate of single platelet aggregation was still highest at Ḡ = 41.9 s1 where large aggregates up to several millimeters in diameter containing rbc formed by t = 43 s. At this ADP concentration, aggregate size was still limited at Ḡ ≥335 s−1 but the rate of single platelet aggregation was markedly greater than at 0.2 pM ADP. By t = 43 s, no single platelets remained and rbc were not incorporated into aggregates. Although aggregate size increased slowly, large aggregates eventually formed. White blood cells were not significantly incorporated into aggregates at any shear rate or ADP concentration. Since the present technique did not induce platelet thromboxane A2 formation or cause cell lysis, these experiments provide evidence for a purely mechanical effect of rbc in augmenting platelet aggregation in WB.

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