Title

Supervised Classification of White Blood Cells By Fusion of Color Texture Features and Neural Network

Document Type

Article

Publication Date

2011

Publication Title

International Journal of Computational Intelligence and Applications

Volume

10

Issue

4

First page number:

471

Last page number:

471

Abstract

Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test sets.

Keywords

Blood cell count--Automation; Image analysis--Data processing; Leucocytes; Neural networks (Computer science)

Disciplines

Biomedical | Biomedical Engineering and Bioengineering | Cells | Electrical and Computer Engineering | Medicine and Health Sciences

Language

English

Permissions

Use Find in Your Library, contact the author, or use interlibrary loan to garner a copy of the article. Publisher copyright policy allows author to archive post-print (author’s final manuscript). When post-print is available or publisher policy changes, the article will be deposited

Identifier

DOI: 10.1142/S1469026811003197

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