DocumentCode
3182192
Title
A quantitative analysis of F-actin features and distribution in fluorescence microscopy images to distinguish cells with different modes of motility
Author
Jie Cheng ; Xiaoping Zhu ; Hao Cheng ; Hong Zhao ; Wong, Stephen T. C.
Author_Institution
Syst. Med. & Bioeng. Dept., Weill Cornell Med. Coll., Houston, TX, USA
fYear
2013
fDate
3-7 July 2013
Firstpage
136
Lastpage
139
Abstract
Actin is one of the most abundant proteins in eukaryote cells, playing a key role in cell dynamic morphological alterations and tumor metastatic spread. To investigate the relationship between the distribution patterns of actin and the aggressiveness of cancer cells, we developed an image analysis framework for quantifying cell F-actin distributions examined with fluorescence microscopy. The images are first segmented with multichannel information of both F-actin and nuclear staining. Using the watershed method and Voronoi tessellation, the cells can be well segmented based on both F-actin and nuclear information. Altogether, sixteen F-actin distribution features are calculated for each individual cell. A linear Support Vector Machine (SVM) is then applied in the feature space to separate cells with different modes of motility. Our results show that cells with different modes of motility can be distinguished by F-actin distributions. To our knowledge, this is the first study managing to distinguish cancer cells with different aggressiveness based on quantitative analysis of F-actin distribution patterns.
Keywords
cancer; cell motility; fluorescence; image segmentation; medical image processing; molecular biophysics; optical microscopy; proteins; support vector machines; tumours; F-actin distribution feature; SVM; Voronoi tessellation; cancer cells; cell F-actin distributions; cell dynamic morphological alterations; cell motility; eukaryote cells; feature space; fluorescence microscopy image; image analysis framework; image segmentation; linear support vector machine; multichannel information; nuclear information; nuclear staining; proteins; quantitative analysis; tumor metastatic spread; watershed method; Biomedical imaging; Cancer; Feature extraction; Image segmentation; Microscopy; Nuclear measurements; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location
Osaka
ISSN
1557-170X
Type
conf
DOI
10.1109/EMBC.2013.6609456
Filename
6609456
Link To Document