Home
  • English
  • ÄŒeÅ¡tina
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • LatvieÅ¡u
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Log In
    New user? Click here to register. Have you forgotten your password?
Home
  • Browse Our Collections
  • Publications
  • Researchers
  • Research Data
  • Institutions
  • Statistics
    • English
    • ÄŒeÅ¡tina
    • Deutsch
    • Español
    • Français
    • Gàidhlig
    • LatvieÅ¡u
    • Magyar
    • Nederlands
    • Português
    • Português do Brasil
    • Suomi
    • Log In
      New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Resources
  3. UniMAP Index Publications
  4. Publications 2020
  5. Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes
 
Options

Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes

Journal
Engineering and Applied Science Research
ISSN
25396161
Date Issued
2020-06-11
Author(s)
Anas Mohd Noor
Universiti Malaysia Perlis
Haniza Yazid
Universiti Malaysia Perlis
Zulkarnay Zakaria
Universiti Malaysia Perlis
Aishah Mohd Noor
Universiti Malaysia Perlis
DOI
10.14456/easr.2020.13
Abstract
Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.
Subjects
  • Histogram of oriented...

File(s)
Research repository notification.pdf (4.4 MB)
google-scholar
Views
Downloads
  • About Us
  • Contact Us
  • Policies