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Integrating multi-scale blob/curvilinear detector techniques and multi-level sets for automated segmentation of stem cell images

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5 Author(s)
Huiming Peng ; Dept. of Radiol., Weill Cornell Med. Coll., Houston, TX, USA ; Xiaobo Zhou ; Fuhai Li ; Xiaofeng Xia
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Studies of differentiation abilities of stem cells have been attracting a lot of attention over the last years. Microscopy can be used to record details of the differentiation process of stem cells under different perturbations and is an important tool for studying stem cell differentiation. Since it is infeasible to quantitatively analyze a huge amount of image data manually, automated image analysis systems are urgently needed. However, the complicated morphological appearances of stem cells are challenging to the existing segmentation methods. Herein, we propose a new, automated scheme for stem cell segmentation. This scheme first uses the multi-scale blob and curvilinear structure detectors to delineate the skeletons of stem cells quickly and then segment out stem cells by refining the skeletons to the cell boundaries using multi-level sets. The initial experimental results indicate the effectiveness of the proposed scheme.

Published in:

Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on

Date of Conference:

June 28 2009-July 1 2009