• Supervised Classification of W...

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

Title:
Supervised Classification of White Blood Cells By Fusion of Color Texture Features and Neural Network
Authors:
Wiselin Jiji, G., Selvaraj, Henry, Evelin Suji, G.
Source:
Electrical & Computer Engineering Faculty Research
Publisher Information:
Digital Scholarship@UNLV
Publication Year:
2011
Collection:
University of Nevada, Las Vegas: Digital Scholarship@UNLV
Subject Terms:
Blood cell count--Automation, Image analysis--Data processing, Leucocytes, Neural networks (Computer science), Biomedical, Biomedical Engineering and Bioengineering, Cells, Electrical and Computer Engineering, Medicine and Health Sciences
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
Relation:
https://digitalscholarship.unlv.edu/ece_fac_articles/269
Availability:
https://digitalscholarship.unlv.edu/ece_fac_articles/269
Accession Number:
edsbas.CCE5974B
Database:
BASE

Weitere Informationen

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.