Automated tissue image analysis: Difference between revisions

Revision as of 10:23, 8 April 2013

Revision as of 07:10, 8 May 2013

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==Processes==

The [[United States of America|United States]] [[Food and Drug Administration]] classifies these systems as [[medical device]]s, under the general instrumentation category of [[automatic test equipment]].<ref>{{cite webbook|url=http://books.google.com/books?id=nEHLxh9wxZIC&pg=PA80&lpg=PA80&dq=%22FDA+automatic+test+equipment%22&source=bl&ots=nO34ROCJsK&sig=NZiXuURP1oUelJRfQ_JeO4eGzNg&hl=en&ei=UXRhSuf5IYiosgPD6Pxm&sa=X&oi=book_result&ct=result&resnum=1 |title=Testing Computer Systems for FDA/MHRA Compliance - David Stokes - Google Books |publisher=Books.google.com |date= |accessdate=2012-07-12}}</ref>

ATIS have seven basic processes (sample preparation, image acquisition, image analysis, results reporting, data storage, network communication, and self-system diagnostics) and realization of these functions highly accurate hardware and well-integrated, complex, and expensive software.<ref>{{cite web|url=http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TF4-4J3NYCV-2&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=960952697&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b3f5dd0c610bf2586d1b0d99d784dff3 |title=Analytica Chimica Acta - Advances in cancer tissue microarray technology: Towards improved understanding and diagnostics |publisher=ScienceDirect.com |date= |accessdate=2012-07-12}}</ref>

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===Analysis===

Image analysis involves complex computer algorithms which identify and characterize cellular color, shape, and quantity of the tissue sample using image pattern recognition technology based on [[vector quantization]]. Vector representations of objects in the image, as opposed to bitmap representations, have superior zoom-in ability. Once the sample image has been acquired and resident in the computer's random access memory as a large array of 0's and 1's, a programmer knowledgeable in cellular architecture can develop deterministic [[algorithms]] applied to the entire memory space to detect cell patterns from previously defined cellular structures and formations known to be significant .<ref name="han12cell">{{cite journal| author=Han, J.W., Breckon, T.P., Randell, D.A., Landini, G.| title=The Application of Support Vector Machine Classification to Detect Cell Nuclei for Automated Microscopy| journal=Machine Vision and Applications| year=2012| volume=23| number=1| pages=15-2415–24| publisher=Springer| doi=10.1007/s00138-010-0275-y| url=http://www.cranfield.ac.uk/~toby.breckon/publications/papers/han12cell.pdf| accessdate=8 April, 2013}}</ref>.

The aggregate algorithm outcome is a set of measurements that is far superior to any human sensitivity to intensity or [[luminance]] and color hue, while at the same time improving test consistency from eyeball to eyeball.{{Citation needed|date=August 2010}}