Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very
important process for early detection of related disease such as malaria and anemia before
suitable follow up treatment can be proceed. Some of the human disease can be showed
by counting the number of red blood cells. Red blood cell count gives the vital information
that help diagnosis many of the patient’s sickness. Conventional method under blood
smears RBC diagnosis is applying light microscope conducted by pathologist. This
method is time-consuming and laborious. In this project an automated RBC counting 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
thresholding. Then it continues with RBCs counting by using two different algorithms
which are the watershed segmentation based on distance transform, and the second one is
the artificial neural network (ANN) classification with fitting application depend on
regression method. Before applying ANN classification there are step needed to get
feature extraction data that are the data extraction using moment invariant. There are still
weaknesses and constraints due to the image itself such as color similarity, weak edge
boundary, overlapping condition, and image quality. Thus, more study must be done to
handle those matters to produce strong analysis approach for medical diagnosis purpose.
This project build a better solution and help to improve the current methods so that it can
be more capable, robust, and effective whenever any sample of blood cell is analyzed. At
the end of this project it conducted comparison between 20 images of blood samples taken
from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM).
The proposed method has been tested on blood cell images and the effectiveness and
reliability of each of the counting method has been demonstrated
