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Image Classification of Vascular Smooth Muscle Cells

Published

Author(s)

Michael Grasso, Ronil Mokashi , Alden A. Dima, Antonio Cardone, Kiran Bhadriraju, Anne L. Plant, Mary C. Brady, Yaacov Yesha, Yelena Yesha

Abstract

The traditional method of cell microscopy can be subjective, due to observer variability, a lack of standardization, and a limited feature set. To address this challenge, we developed an image classifier using a machine learning approach. Our system was able to classify cytoskeletal changes in A10 rat smooth muscle cells with an accuracy of 85% to 99%. These cytoskeletal changes correspond to cell-to-cell and cell-to-matrix interactions. Analysis of these changes may be used to better understand how these interactions correspond to certain physiologic processes.
Proceedings Title
1st ACM International Health Informatics Symposium
Conference Dates
November 11-12, 2010
Conference Location
Arlington, VA, US

Keywords

digital image processing, machine learning, molecular biology

Citation

Grasso, M. , Mokashi, R. , Dima, A. , Cardone, A. , Bhadriraju, K. , Plant, A. , Brady, M. , Yesha, Y. and Yesha, Y. (2010), Image Classification of Vascular Smooth Muscle Cells, 1st ACM International Health Informatics Symposium , Arlington, VA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=905981 (Accessed May 21, 2024)

Issues

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Created November 10, 2010, Updated October 12, 2021