Red blood cells segmentation and estimation
The erythrocytes are the most numerous blood cells in human body and it also called red blood cells. The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the n...
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2012
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my-utm-ep.321752018-05-27T07:42:26Z Red blood cells segmentation and estimation 2012-01 Mansor, Muhammad Asraf TK Electrical engineering. Electronics Nuclear engineering The erythrocytes are the most numerous blood cells in human body and it also called red blood cells. The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in blood sample image. The proposed system takes an input, color image of stained peripheral bold smear images. Since the object of interest is the red blood cells, the system is capability to detect or differentiate between the red blood cells with other blood cell based on size of object. In order to detect red blood cells, the segmentation and extraction step must come early before proceeded to the detection process. In addition this system also can provide the capability to estimate the number of red blood cells. This process is based on the circle detection process by considering that the red blood cells always in normal radius and circle shape of red blood cells. Thus, the result presented here is based on images with normal blood cells. The tested data consisting 20 samples produced the accurate estimation rate close to 96% from manual counting. 2012-01 Thesis http://eprints.utm.my/id/eprint/32175/ http://eprints.utm.my/id/eprint/32175/1/MuhammadAsrafMansorMFKE2012.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering |
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Universiti Teknologi Malaysia |
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UTM Institutional Repository |
| language |
English |
| topic |
TK Electrical engineering Electronics Nuclear engineering |
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TK Electrical engineering Electronics Nuclear engineering Mansor, Muhammad Asraf Red blood cells segmentation and estimation |
| description |
The erythrocytes are the most numerous blood cells in human body and it also called red blood cells. The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in blood sample image. The proposed system takes an input, color image of stained peripheral bold smear images. Since the object of interest is the red blood cells, the system is capability to detect or differentiate between the red blood cells with other blood cell based on size of object. In order to detect red blood cells, the segmentation and extraction step must come early before proceeded to the detection process. In addition this system also can provide the capability to estimate the number of red blood cells. This process is based on the circle detection process by considering that the red blood cells always in normal radius and circle shape of red blood cells. Thus, the result presented here is based on images with normal blood cells. The tested data consisting 20 samples produced the accurate estimation rate close to 96% from manual counting. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Mansor, Muhammad Asraf |
| author_facet |
Mansor, Muhammad Asraf |
| author_sort |
Mansor, Muhammad Asraf |
| title |
Red blood cells segmentation and estimation |
| title_short |
Red blood cells segmentation and estimation |
| title_full |
Red blood cells segmentation and estimation |
| title_fullStr |
Red blood cells segmentation and estimation |
| title_full_unstemmed |
Red blood cells segmentation and estimation |
| title_sort |
red blood cells segmentation and estimation |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Electrical Engineering |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2012 |
| url |
http://eprints.utm.my/id/eprint/32175/1/MuhammadAsrafMansorMFKE2012.pdf |
| _version_ |
1747815939671326720 |