scholarly journals A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing

2021 ◽  
Vol 11 (6) ◽  
pp. 5060
Author(s):  
Mohammed Al-Momin ◽  
Ammar Almomin
Keyword(s):  
Image Processing ◽  
Blood Cells ◽  
White Blood Cells ◽  
Problem Of Time ◽  
Blood Constituents ◽  
Sickle Cells ◽  
Blood Disorder ◽  
Blood Smears ◽  
High Level ◽  
Processing Techniques

<span lang="EN-US">The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy.</span>

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%.

scholarly journals The prevalence of leukocyte abnormalities among Sudanese patients with sickle cell disease

2021 ◽  
Vol 9 (1) ◽  
pp. 262-267
Author(s):  
Tarig Osman Khalafallah Ahmed ◽  
Ekhlas Alrasheid Abu Elfadul ◽  
Ahmed A. Agab Eldour ◽  
Omer Ibrahim Abdallah Mohammed
Keyword(s):  
Sickle Cell Disease ◽  
Cell Volume ◽  
Sickle Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Blood Film ◽  
Cell Disease ◽  
Mean Cell Volume ◽  
Blood Disorder ◽  
Age Range

Sickle cell disease (SCD) is an inherited blood disorder that affects red blood cells. The study was conducted in Elobied town during the period May 2011 to September 2011. The aim of this study is to detect the abnormalities of leucocytes among sickle cell anemic patients. 40 sickle cell anemic patients; age range between 8 months to 23 years. Blood sample was taken for all patients and the laboratory investigation were performed using automated estimation for: hemoglobin (Hb), Packed cell volume (PCV), red cell count (RBCs), mean cell volume (MCV), mean cell hemoglobin (MCH), mean cell concentration (MCHC), and total white blood cells, comment on blood film using manual methods. The conclusion of this study there is increase in total white blood cells with shift to left in neutrophil precursor in sickle cell patients with complications ,the most immature cells are band form, myelocytes and metamyelocytes, and there also lymphocytosis and neutrophilia which has been increases in response to infections.

scholarly journals White Blood Cell Image Classification Using Deep Learning

2021 ◽  
Vol 7 (09) ◽  
pp. 7-12
Author(s):  
Neerukattu Indrani and Chiraparapu Srinivasa Rao
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Training Phase ◽  
Counting System ◽  
Cell Image ◽  
Area Of Interest ◽  
Manual Counting ◽  
Blood Smears ◽  
Microscopic Inspection

The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status. For example, the presence of infections, leukemia, and some particular kinds of cancers can be diagnosed based on the results of the classification and the count of white blood cells. The traditional method for the differential blood count is performed by experienced operators. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. Obviously, this manual counting process is very tedious and slow. In addition, the cell classification and counting accuracy may depend on the capabilities and experiences of the operators. Therefore, the necessity of an automated differential counting system becomes inevitable. In this paper, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results within the seconds also.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 327-347 ◽  
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

scholarly journals Effects of Cinnamomum zeylanicum treatment on radiolabeling of blood bonstituents and morphology of red blood cells in Wistar rats

2008 ◽  
Vol 51 (spe) ◽  
pp. 143-149 ◽  
Author(s):  
Mônica Oliveira Benarroz ◽  
Gabrielle de Souza Rocha ◽  
Márcia Oliveira Pereira ◽  
Mauro Geller ◽  
Adenilson de Souza da Fonseca ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Wistar Rats ◽  
Membrane Structures ◽  
Blood Constituents ◽  
Cinnamon Extract ◽  
Blood Smears ◽  
Transport Of Ions ◽  
Different Doses

The aim of this study was to evaluate the effect of in vivo treatment with an aqueous cinnamon extract on the labeling of blood constituents with 99mTc and on the morphology of red blood cells from Wistar rats. Animals were treated with cinnamon extract at different doses and for different periods of time. As controls, animals treated with 0.9% NaCl. Labeling of blood constituents with 99mTc was performed. Plasma, blood cells and insoluble fractions were isolated. Radioactivity in each fraction was counted and the percentage of radioactivity (%ATI) was calculated. Also, blood smears were prepared to morphological analysis of red blood cells from. Data showed that in vivo cinnamon extract did not significantly (p>0.05) modify the %ATI of blood constituents and morphology of red blood cells. The results suggest that in vivo aqueous cinnamon could not affect the membrane structures involved in transport of ions or the oxidation state of stannous and pertechnetate ions.

scholarly journals II. APLASIA OF MARROW AND FATAL INTOXICATION IN DOGS PRODUCED BY ROENTGEN RADIATION OF ALL BONES

1931 ◽  
Vol 53 (3) ◽  
pp. 421-435 ◽  
Author(s):  
Samuel S. Shouse ◽  
Stafford L. Warren ◽  
George H. Whipple
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Germinal Centers ◽  
Fat Cells ◽  
Red Cell ◽  
Fatal Intoxication ◽  
Blood Smears ◽  
Recent Origin ◽  
Acute Reaction ◽  
Spleen And Lymph Nodes

Constant findings were obtained in the acute reaction to the specified amount of heavily filtered radiation over the bony skeleton. 1. There develops without warning a short and fatal intoxication on the 8th or 9th day after the exposure to the radiation. 2. A profound leucopenia appears after 5 to 6 days and is maintained in the peripheral blood (200 white blood cells or less per c. mm.) for the 2 to 3 days before death. 3. The platelets suddenly disappear from the blood smears the day before death. This has some bearing on the life cycle of the platelet. 4. All of the organs and body structures present extensive and generalized capillary hemorrhage of recent origin. 5. The substance of the spleen and lymph nodes is greatly reduced and the germinal centers are visible only as remnants. 6. The red cell hematocrit reading drops from about 50 per cent or normal to approximately 40 per cent. 7. The bone marrow is depleted of all its cells except the connective tissue and fat cells, blood vessel endothelium, phagocytes filled with brown granules, and occasional normoblasts.

scholarly journals Segmentation of White Blood Cells and Lymphoblast Cells Using Moving K-Means

2018 ◽  
Vol 8 (2) ◽  
pp. 211
Author(s):  
Ika Candradewi ◽  
Reno Ghaffur Bagasjvara
Keyword(s):  
Image Processing ◽  
Acute Lymphoblastic Leukemia ◽  
Digital Image Processing ◽  
Digital Image ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Image Processing Technique ◽  
Cell Segmentation ◽  
Lymphoblast Cells

One of the diagnosis procedures for acute lymphoblastic leukemia is screening for blood cells by expert operator using microscope. This process is relatively long and will slow healing process of this disease which need fast treatment. Another way to screen this disease is by using digital image processing technique in microscopic image of blood smears to detect lymphoblast cells and types of white blood cells. One of essential step in digital image processing is segmentation because this process influences the subsequent process of detecting and classifying Acute Lymphoblastic Leukemia disease. This research performed segmentation of white blood cells using moving k-means algorithm. Some process are done to remove noise such as red blood cells and reduce detection errors such as white blood cells and/or lymphoblastic cell  that’s appear overlap. Postprocessing are performed to improve segmentation quality and to separate connected white blood cell. The dataset in this study has been validated with expert clinical pathologists from Sardjito Regional General Hospital, Yogyakarta, Indonesia. This research produces systems performance with results in sensitivity of 85.6%, precision 82.3%, Fscore of 83,9% and accuracy of 72.3%. Based on the results of the testing process with a much larger number of datasets on the side of the variations level of cell segmentation difficulties both in terms of illumination and overlapping cell, the method proposed in this study was able to detect or segment overlapping white blood cells better.

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