Automated tissue image analysis: Difference between revisions

Revision as of 07:10, 8 May 2013

Revision as of 00:00, 5 November 2013

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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=15–24| publisher=Springer| doi=10.1007/s00138-010-0275-y| url=http://www.cranfielddurham.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}}