Browse > Article
http://dx.doi.org/10.6109/jkiice.2014.18.2.431

An Efficient Segmentation System for Cell Images By Classifying Distributions of Histogram  

Cho, Migyung (Department of Media Engineering, Tongmyong University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.2, 2014 , pp. 431-436 More about this Journal
Abstract
Cell segmentation which extracts cell objects from background is one of basic works in bio-imaging which analyze cell images acquired from live cells in cell culture. In the case of clear images, they have a bi-modal histogram distribution and segmentation of them can easily be performed by global threshold algorithm such as Otsu algorithm. But In the case of degraded images, it is difficult to get exact segmentation results. In this paper, we developed a cell segmentation system that it classify input images by the type of their histogram distribution and then apply a proper segmentation algorithm. If it has a bi-modal distribution, a global threshold algorithm is applied for segmentation. Otherwise it has a uni-modal distribution, our algorithm is performed. By experimentation, our system gave exact segmentation results for uni-modal cell images as well as bi-modal cell images.
Keywords
Segmentation; Cell images; Histogram; Uni-modal distribution; Bi-modal distribution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P.K Sahoo, S. Soltani, A.K.C Wong, "A survey of thresholding techniques", Computer Vision, Graphics, and Image Processing, Vol. 41, Issue 2, pp. 233-260, 1988.   DOI   ScienceOn
2 Xian Du, Sumeet Dua, "Segmentation of Fluorescence Microscopy Cell Image Using Unsupervised Mining," The Open Medical Information Journal, Vol.4, pp.41-49, 2010.   DOI
3 Migyung Cho, "An Algorithm for Determining Histogram Type of a Cell Image Using Polygonal Approximation," International Journal of Advancements in Computing Technology, Vol.5 No.13, pp.229-234, Sept., 2013.
4 Thouis R. Jones, Anne Carpenter, Polina Golland, "Voronoibased segmentation of cells on image manifolds", Lecture Notes of Computer Science, Vol. 3765, pp 535-543, 2005.
5 Bazi Y, Rruzzone L, Melgani F., "Image thresholding based on the EM algorithm and the generalized Gaussian distribution", Pattern Recognition Letter, Vol. 40, pp.619-634, 2007.   DOI   ScienceOn
6 Nobuyuki Otsu, "A threshold Selection Method from Gray-Level Histograms", IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-9, No. 1, pp62-66, Jan., 1979.
7 Migyung Cho and Jaesool Shim "Segmentation Algorithm of Fluorescence Microscopy Cell Images using Area Partition," in Proceeding of the 3rd International Conference on Convergence Technology 2013, Chiang Mai, Thailand, pp. 402-403, 2013.
8 Feixianng Yan, Hong Zhang, C. Ronald Kube, "A multistage adaptive thresholding method," Pattern Recognition Letters, vol. 26, pp. 1183-1191, Dec. 2005.   DOI   ScienceOn
9 Nicolas Coudray, Jean-Luc Buessler, Jean-Philippe Urban, "Robust threshold estimation for images with unimodal histograms," Pattern Recognition Letters, vol. 31, pp. 1010-1019, Jan. 2010.   DOI