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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 758587, 10 pages
http://dx.doi.org/10.1155/2014/758587
Research Article

Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2College of Life Sciences, Fujian Normal University, Fuzhou, Fujian 350007, China
3Department of Informatics, University of Hamburg, 22527 Hamburg, Germany

Received 25 March 2014; Accepted 10 May 2014; Published 28 May 2014

Academic Editor: Carlo Cattani

Copyright © 2014 Lei Chen 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

Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.