Learning tools for blood dell segmentation and extraction techniques
Blood cell segmentation and identification is vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when mas...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/33211/1/PoonCheeLimMFKE2013.pdf |
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| Summary: | Blood cell segmentation and identification is vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when massive amounts of blood samples are required to be processed by the haematologist or Medical Laboratory Technicians. The time and skill required for the task limits the speed and accuracy with which the blood sample can be processed. This project aims to provide user-friendly software based on MATLAB allowing for quick user interaction with a simple tool for the segmentation and identification of red and white blood cells from a provided image. The project presents the solution in a modular framework allowing for future development within a structured environment. In order to perform the segmentation, this project uses k-means clustering and colour based segmentation using International Commission on Illumination L*a*b* (CIELAB) colour space coupled with polygon information of the region of interest. The project integrates these methods into a flow within a Graphical User Interface (GUI) with customizable variables to handle changing input images. The result of the project is a working GUI with the capability to accept user interaction. The completed project is able to obtain quick and accurate blood cell segmentation of both red and white blood cells. The accuracy of this project ranges from 64% to 87% depending on the type of processing used and the type of cells being extracted. |
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