title: Efficient leukocyte segmentation and recognition in peripheral blood image.
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.
authors.affiliation: Hazara University, Mansehra, Pakistan.
authors.forename: Syed H
authors.initials: SH
authors.lastname: Shirazi
authors.affiliation: Hazara University, Mansehra, Pakistan.
authors.forename: Arif Iqbal
authors.initials: AI
authors.lastname: Umar
authors.affiliation: Hazara University, Mansehra, Pakistan.|GGPGC, Abbottabad, HED, KPK, Pakistan.
authors.forename: Saeeda
authors.initials: S
authors.lastname: Naz
authors.affiliation: King Saud Bin Abdul Aziz University for Health Sciences, Riyadh, Saudi Arabia.
authors.forename: Muhammad I
authors.initials: MI
authors.lastname: Razzak
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007958:Leukocyte Count / Q000295:instrumentation*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Leukocyte; blood cell segmentation; curvelet; leukocyte classification; microscope image analysis