Efficient leukocyte segmentation and recognition in peripheral blood image

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
Syed H ShiraziMuhammad I Razzak

Abstract

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. The aim of this work is to develop an efficient leukocyte cell segmentation and classification system. 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. As a result, the...Continue Reading

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Citations

Nov 9, 2016·Medical & Biological Engineering & Computing·Jianwei ZhaoFeilong Cao
May 18, 2020·Medical & Biological Engineering & Computing·Yapin Wang, Yiping Cao
Mar 25, 2020·Neural Computing & Applications·Muhammad Imran RazzakGuandong Xu
Dec 18, 2020·Computer Methods and Programs in Biomedicine· Anita, Anupam Yadav
Aug 3, 2021·Computational and Mathematical Methods in Medicine·Jin ChenHaihua An
Oct 9, 2021·Biomedical Physics & Engineering Express·Diana BabyM M Anishin Raj

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