الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Human blood cells consist of Red Blood Cells ‘RBCs’, White Blood
Cells ‘WBCs’, and platelets. The red blood cells are responsible for the
oxygen transportation to all cells and tissues, and transmission of
carbon dioxide to the lungs. The white blood cells have several types,
which are essential in the human body’s immune system for protection
from viruses, germs and bacteria. The microscopic images of the blood
cells (BCs) have different nature, shape, and color. Each type of BCs in
the microscopic images provides information that helps in the diagnosis
and treatment of various diseases, such as leukemia, malaria, and
anemia. The BCs segmentation has an impact role in the diagnosis and
treatment such diseases. It is also a crucial stage in image processing
steps for successful further stages, including feature extraction, and
classification. However, blood cells segmentation to determine the
region of interested (ROI) is considered a challenging task due to the
cells’ complex shape/ nature (texture, color, shape, and size), the
ambiguity, uncertainty, and inconsistencies in the microscopic captured
images due to the varying illumination and the existence of overlapped
cells. This thesis proposed a new weighted transformation-based
segmentation method to detect five types of BCs. This method depends
mainly on the discrete cosine transformation technique (DCT), which
deals with real values instead of complex values, reduces the
computation complexity, enhances the image contrast, and reduces the
illumination variance in an image by removing the low-frequency in the
DCT coefficients. A threshold was applied to the proposed weighted
segmentation to determine the ROI. Different evaluation metrics were
calculated to assess the performance of the proposed segmentation method. Moreover, a comparative study with other well-known
methods was conducted. The results proved the superiority of the
proposed weighted transformation–based segmentation method, which
achieved average segmentation metrics over the different five types:
99.8% accuracy, 99.9% specificity, 89.3% sensitivity, 91.8% Dice and
85.3% Jac.