Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE)

BMC Bioinformatics

Research

Meenakshisundaram Kandhavelu¹  Andre S Ribeiro¹  Sharif Chowdhury¹  Olli Yli-Harja² 

[1] Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland;Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland;Institute for Systems Biology, 401 Terry Avenue North, 98109-5234, Seattle, WA, USA;

关键词: Decomposition Level;    Foreground Object;    Segmentation Accuracy;    Cell Width;    Initial Segmentation;   

DOI  :  10.1186/1471-2105-14-S10-S8

来源: Springer

【 摘 要 】

BackgroundCell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters.MethodsMAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained.ConclusionsWe performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

【 授权许可】

Unknown   
© Chowdhury et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.