Sickle cell identification using image processing and red blood cell morphological characteristics

Blood is the most vital liquid that helps sustain the healthiness and life of the human body. Biologically, normal red blood cells have circular shapes and play a key role in transporting oxygen and nutrient to tissues. Red blood cells are bendable, which allows them to pass through the veins and ar...

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Main Author:	Abdul Malik, Umar Turaki
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/96902/1/UmarTurakiAbdulMalikMFABU2021.pdf.pdf
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spelling	my-utm-ep.969022022-08-28T04:02:22Z Sickle cell identification using image processing and red blood cell morphological characteristics 2021 Abdul Malik, Umar Turaki TK Electrical engineering. Electronics Nuclear engineering Blood is the most vital liquid that helps sustain the healthiness and life of the human body. Biologically, normal red blood cells have circular shapes and play a key role in transporting oxygen and nutrient to tissues. Red blood cells are bendable, which allows them to pass through the veins and arteries smoothly. Sadly, there are exceptional individuals with abnormal blood cells call sickle cell disease. The physical shape of the abnormal blood cells is in sickle/crescent form. Sickle cell disease is hereditary, and a person becomes affected if at least one of the parents has the abnormal haemoglobin S gene. The danger of sickle cells is that they inflict many severe health conditions such as pain, tiredness, jaundice, kidney problem, and other critical illnesses. For many years, managing and diagnosing sickle patients is performed by collecting blood samples to manually observe the irregular shapes of the red blood cells using a microscope. This process is time-consuming and results in errors for large samples of blood. In this thesis, a compelling image processing method is proposed to optimize the detection of abnormality in human blood cells with the deep learning technique. Ten images of red blood cells were randomly collected from the online source using the Google search engine. Each image was analyzed using MATLAB codes for image processing using the blood cell area, eccentricity, diameter, extension, and form factor as input parameters. The study results show that the proposed technique has 71 – 100 percent accuracy, far higher than what is obtainable in the manual method. This technique can serve and enhance the current manual method of sickle cell segmentation because it is faster and more accurate. 2021 Thesis http://eprints.utm.my/id/eprint/96902/ http://eprints.utm.my/id/eprint/96902/1/UmarTurakiAbdulMalikMFABU2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:142169 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abdul Malik, Umar Turaki Sickle cell identification using image processing and red blood cell morphological characteristics
description	Blood is the most vital liquid that helps sustain the healthiness and life of the human body. Biologically, normal red blood cells have circular shapes and play a key role in transporting oxygen and nutrient to tissues. Red blood cells are bendable, which allows them to pass through the veins and arteries smoothly. Sadly, there are exceptional individuals with abnormal blood cells call sickle cell disease. The physical shape of the abnormal blood cells is in sickle/crescent form. Sickle cell disease is hereditary, and a person becomes affected if at least one of the parents has the abnormal haemoglobin S gene. The danger of sickle cells is that they inflict many severe health conditions such as pain, tiredness, jaundice, kidney problem, and other critical illnesses. For many years, managing and diagnosing sickle patients is performed by collecting blood samples to manually observe the irregular shapes of the red blood cells using a microscope. This process is time-consuming and results in errors for large samples of blood. In this thesis, a compelling image processing method is proposed to optimize the detection of abnormality in human blood cells with the deep learning technique. Ten images of red blood cells were randomly collected from the online source using the Google search engine. Each image was analyzed using MATLAB codes for image processing using the blood cell area, eccentricity, diameter, extension, and form factor as input parameters. The study results show that the proposed technique has 71 – 100 percent accuracy, far higher than what is obtainable in the manual method. This technique can serve and enhance the current manual method of sickle cell segmentation because it is faster and more accurate.
format	Thesis
qualification_level	Master's degree
author	Abdul Malik, Umar Turaki
author_facet	Abdul Malik, Umar Turaki
author_sort	Abdul Malik, Umar Turaki
title	Sickle cell identification using image processing and red blood cell morphological characteristics
title_short	Sickle cell identification using image processing and red blood cell morphological characteristics
title_full	Sickle cell identification using image processing and red blood cell morphological characteristics
title_fullStr	Sickle cell identification using image processing and red blood cell morphological characteristics
title_full_unstemmed	Sickle cell identification using image processing and red blood cell morphological characteristics
title_sort	sickle cell identification using image processing and red blood cell morphological characteristics
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/96902/1/UmarTurakiAbdulMalikMFABU2021.pdf.pdf
_version_	1747818695491584000

Sickle cell identification using image processing and red blood cell morphological characteristics

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