An Automated Method for Differential Blood Counting Using Microscope Color Image of Isolated WBC

Author:

Koppar Anant R.1,Sridhar Venugopalachar1

Affiliation:

1. P E T Research Center, India

Abstract

Healthcare Delivery Systems are becoming overloaded in developed and developing countries. It is imperative that more efficient and cost effective processes be employed by innovative applications of technology in the delivery system. One such process in Haematology that needs attention is “Generation of report on the Differential Count of Blood”. Most rural centers in India still employ traditional, manual processes to identify and count White Blood Cells under a microscope. This traditional method of manually counting the white blood cells is prone to human error and time consuming. Medical Imaging with innovative application of algorithms can be used for recognizing and analyzing the images from blood smears to provide an efficient alternative for differential counting and reporting. In this regard, the objective of this paper is to provide a simple and pragmatic software system built on innovative yet simple imaging algorithms for achieving better efficiency and accuracy of results. The resulting work-flow process has enabled truly practical tele-pathology by enabling e-collaboration between lesser skilled technicians and more skilled experts, which cuts down the total turnaround time for differential count reporting from days to minutes. The system can be extended to detect malarial parasites in blood and also cancerous cells.

Publisher

IGI Global

Subject

Health Informatics,Computer Science Applications

Reference28 articles.

1. Bamford, P., & Lovell, B. (2001). Method for accurate unsupervised cell nucleus segmentation. In Proceedings of the EMBS Conference (pp. 2704-2708).

2. Beckman Coulter, Inc. (2006). The Coulter Principles. Retrieved from http://www.beckmancoulter.com/products/instrument/partchar/technology/coulterprinciple.asp

3. Bikhet, F., Darwish, A. M., Tolba, H. A., & Shaheen, S. I. (n.d.). Segmentation and classification of white blood cells.

4. CellaVision AB. (2006-2009). Automated digital cell analysis. Retrieved from http://www.cellavision.com

5. Color image segmentation: advances and prospects

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3