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|Author(s):||Michael Grasso; Ronil Mokashi; Alden A. Dima; Antonio Cardone; Kiran Bhadriraju; Anne L. Plant; Mary C. Brady; Yaacov Yesha; Yelena Yesha;|
|Title:||Image Classification of Vascular Smooth Muscle Cells|
|Published:||November 11, 2010|
|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:||1st ACM International Health Informatics Symposium|
|Dates:||November 11-12, 2010|
|Keywords:||digital image processing, machine learning, molecular biology|
|PDF version:||Click here to retrieve PDF version of paper (402KB)|