Digital analysis of microscopic images of blood cells for pathological diagnostics

2004 ◽  
Author(s):  
C. Zambrano Velasco ◽  
F. Correa ◽  
L. Pencue Fierro ◽  
M. Patino ◽  
Jaury Leon-Tellez
Keyword(s):  
Blood Cells ◽  
Digital Analysis ◽  
Microscopic Images

Innovative parameters obtained for digital analysis of microscopic images to evaluate in vitro hemorheological action of anesthetics

2015 ◽  
Author(s):  
Analía I. Alet ◽  
Sabrina Basso ◽  
Marcela Delannoy ◽  
Nicolás A. Alet ◽  
Mabel D'Arrigo ◽  
...  
Keyword(s):  
Digital Analysis ◽  
Microscopic Images

scholarly journals An Enhanced Approach for Automation the Diagnosis of Iron Deficiency Anemia Based on Quantitative Analysis of Red Blood Cells in Intestine Villi Tissue

2018 ◽  
Vol 12 (12) ◽  
pp. 65
Author(s):  
Manar Rizik Al-Sayyed ◽  
Faten Hamad ◽  
Rizik Al-Sayyed ◽  
Hussam N. Fakhouri
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Region Of Interest ◽  
Noise Removal ◽  
Successful Outcome ◽  
Deficiency Anemia ◽  
Microscopic Images ◽  
The Status

Recent years have witnessed a huge revolution in developing automated diagnosis for different diseases such as cancer using medical image processing. Many researchers have been conducted in this field. Analyzing medical microscopic images provide pathology medical track with large information about the status of the patients and the progress of the diseases and help in detecting any pathological changes in tissues. Automation of the diagnosis of these images will lead to a better, faster and enhanced diagnosis for different hematological and histological images. This paper proposes an automated approach for analyzing blood smear microscopic images to help in diagnosing anemia using quantitative analysis of red blood cells in intestine villi tissue. The diagnoses depends on counting the number of blue and red stained blood cells that contain iron in each villi separately, then, it calculates the percentage of blue cells and red cells in the experimented image. The experimental results have shown that using digital image processing techniques through processing the image into different stages as including noise removal, image sharpening, enhancing contrast, find region of interest, isolating color, removing edges, and counting cells leads to a successful outcome and the diagnose of anemia.

scholarly journals Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Mohammad Manthouri ◽  
Zhila Aghajari ◽  
Sheida Safary
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Blood Cells ◽  
Feature Detection ◽  
White Blood Cells ◽  
Processing System ◽  
Health Economy ◽  
Microscopic Images ◽  
Global Issues ◽  
Different Types

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

Application of Biomedical Image Processing in Blood Cell Counting using Hough Transform

2016 ◽  
pp. 359-378 ◽  
Author(s):  
Manali Mukherjee ◽  
Kamarujjaman ◽  
Mausumi Maitra
Keyword(s):  
Blood Cell ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Blood Stream ◽  
Cell Counting ◽  
Biomedical Image Processing ◽  
Microscopic Images ◽  
Biomedical Image ◽  
Rapid Heart Rate

In the field of biomedicine, blood cells are complex in nature. Nowadays, microscopic images are used in several laboratories for detecting cells or parasite by technician. The microscopic images of a blood stream contain RBCs, WBCs and Platelets. Blood cells are produced in the bone marrow and regularly released into circulation. Blood counts are monitored with a laboratory test called a Complete Blood Count (CBC). However, certain circumstances may cause to have fewer cells than is considered normal, a condition which is called “low blood counts”.This can be accomplished with the administration of blood cell growth factors. Common symptoms due to low red blood cells are:fatigue or tiredness, trouble breathing, rapid heart rate, difficulty staying warm, pale skin etc. Common symptoms due to low white blood cells are: infection, fever etc. It is important to monitor for low blood cell count because conditions could increase the risk of unpleasant and sometimes life-threatening side effects.

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