Automated tissue image analysis: Difference between revisions

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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 book|url=https://books.google.com/books?id=nEHLxh9wxZIC&dq=%22FDA+automatic+test+equipment%22&pg=PA80 |title=Testing Computer Systems for FDA/MHRA Compliance - David Stokes - Google Books |date=2003-11-25|accessdate=2012-07-12|isbn=9780849321634|last1=Stokes |first1=David |publisher=Taylor & Francis }}</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 journal|doi=10.1016/j.aca.2005.11.083 |pmid=17723364 |title=Analytica Chimica Acta - Advances in cancer tissue microarray technology: Towards improved understanding and diagnostics|volume=564 |issue=1 |journal=Analytica Chimica Acta |pages=74–81|pmc=2583100|year=2006 |last1=Chen |first1=W. |last2=Foran |first2=D. J. }}</ref>

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[[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.| author2=Breckon, T.P.| author3=Randell, D.A.| author4=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| pages=15–24| publisher=Springer| doi=10.1007/s00138-010-0275-y| issue=1| s2cid=12446454}}</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}}

===Reporting===

The systems have the capability of presenting the resulting data in text and graphically, including on high definition monitors, to the system user. [[Computer printers]], as relatively low image resolution devices, are used mostly to present final [[pathology]] reports that could include text and graphics.{{Citation needed|date=August 2010}}

===Storage===

Storage of the acquired data (graphical digital slide files and text data) involves saving system information in a [[data storage device]] system having at least convenient retrieval, and file management capabilities.{{Citation needed|date=August 2010}}

Medical imaging industry standards includes the [[Picture Archiving and Communication Systems]] (PACS), of European origin, which are image and information management solutions in computer networks that allow hospitals and clinics to acquire, distribute and archive medical images and diagnostic reports across the enterprise. Another standard of European origin is the Data and Picture Archiving and Communication System (DPACS). Although medical images can be stored in various formats, a common format has been Digital Imaging and Communications in Medicine ([[DICOM]]).{{Citation needed|date=August 2010}}

==See also==