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Mathematical Problems in Engineering
Volume 2012, Article ID 474938, 10 pages
http://dx.doi.org/10.1155/2012/474938
Research Article

Incorporating a Local Binary Fitting Model into a Maximum Regional Difference Model for Extracting Microscopic Information under Complex Conditions

1Key Laboratory of E&M, Zhejiang University of Technology, Ministry of Education of China, 310014 Hangzhou, China
2Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
3College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China
4Department of Mathematics, Sapien za University of Rome, P.le A. Moro 2, 00185 Rome, Italy

Received 30 March 2011; Accepted 15 May 2011

Academic Editor: Shengyong Chen

Copyright © 2012 Chunyan Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel region-based method for extracting useful information from microscopic images under complex conditions. It is especially used for blood cell segmentation and statistical analysis. The active model detects several inner and outer contours of an object from its background. The method incorporates a local binary fitting model into a maximum regional difference model. It utilizes both local and global intensity information as the driving forces of the contour model on the principle of the largest regional difference. The local and global fitting forces ensure that local dissimilarities can be captured and globally different areas can be segmented, respectively. By combining the advantages of local and global information, the motion of the contour is driven by the mixed fitting force, which is composed of the local and global fitting term in the energy function. Experiments are carried out in the laboratory, and results show that the novel model can yield good performances for microscopic image analysis.