Affiliations: [a] Hazara University, Mansehra, Pakistan | [b] King Saud Bin Abdul Aziz University for Health Sciences, Riyadh, Saudi Arabia | [c] GGPGC, Abbottabad, HED, KPK, Pakistan
Correspondence:
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Corresponding author: Muhammad Imran Razzak, Health Informatics, CPHHI, Mail Code: 2350, King Saud Bin Abdul Aziz University for Health Sciences, PO BOX: 22490, Riyadh, Saudi Arabia, Post Code: 11426. E-mail:[email protected]
Abstract: BACKGROUND: Blood cell count, also known as differential count of
various types of blood cells, provides valuable information in order to
assess variety of diseases like AIDS, leukemia and blood cancer. Manual
techniques are still used in diseases diagnosis that is very lingering and
tedious process. However, machine based automatic analysis of leukocyte is a
powerful tool that could reduce the human errors, improve the accuracy, and
minimize the required time for blood cell analysis. However, leukocyte
segmentation is a challenging process due to the complexity of the blood
cell image; therefore, this task remains unresolved issue in the blood cell
segmentation. OBJECTIVE: The aim of this work is to develop an efficient
leukocyte cell segmentation and classification system. METHODS: This paper presents an efficient strategy to segment cell
images. This has been achieved by using Wiener filter along with Curvelet
transform for image enhancement and noise elimination in order to elude
false edges. We have also used combination of entropy filter, thresholding
and mathematical morphology for obtaining image segmentation and boundary
detection, whereas we have used back-propagation neural network for
leukocyte classification into its sub classes. RESULTS: As a result, the generated segmentation results are
fruitful in a sense that we have overcome the problem of overlapping cells.
We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15%
accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil
respectively.
