Publication Date:
2011-06-15
Description:
We propose using machine learning techniques to analyze the shape of living cells in phase-contrast microscopy images. Large scale studies of cell shape are needed to understand the response of cells to their environment. Manual analysis of thousands of microscopy images, however, is time-consuming and error-prone and necessitates automated tools. We show how a combination of shape-based and appearance-based features of fibroblast cells can be used to classify their morphological state, using the Adaboost algorithm. The classification accuracy of our method approaches the agreement between two expert observers. We also address the important issue of clutter mitigation by developing a machine learning approach to distinguish between clutter and cells in time-lapse microscopy image sequences. Content Type Journal Article Pages 1-15 DOI 10.1007/s00138-011-0345-9 Authors Diane H. Theriault, Department of Computer Science, Boston University, 111 Cummington St., Boston, MA 02215, USA Matthew L. Walker, Department of Biology, Boston University, 111 Cummington St., Boston, MA 02215, USA Joyce Y. Wong, Department of Biomedical Engineering, Boston University, 111 Cummington St., Boston, MA 02215, USA Margrit Betke, Department of Computer Science, Boston University, 111 Cummington St., Boston, MA 02215, USA Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
Print ISSN:
0932-8092
Electronic ISSN:
1432-1769
Topics:
Computer Science
