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Analysis of red blood cell (RBC) classification using Ni vision builder Ai

Lias, Jalil (2015) Analysis of red blood cell (RBC) classification using Ni vision builder Ai. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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Red blood cell (RBC) diagnosis is very important process for early detection of related disease such as malaria and anaemia before suitable follow up treatment can be proceed. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. Red blood cell counting and classification only rely on the manual visual inspection which is laborious, tedious and required highly skill and experience pathologist to analyse the shape of the red blood cell. In this project an automated RBC counting and classification system is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global threshold of method applied green channel colour image. Then it continues with RBC counting by using particle area and calculator numeric function method. Eventually, Heywood Circularity Factor, Nearest Neighbour, k-Nearest Neighbour and Minimum Mean Distance classifier methods are applied for normal, abnormal and overlap RBC classification. The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the classifier system has been demonstrated.

Item Type: Thesis (Masters)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Depositing User: Normajihan Abd. Rahman
Date Deposited: 13 Mar 2016 08:01
Last Modified: 13 Mar 2016 08:01
URI: http://eprints.uthm.edu.my/id/eprint/7719
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