Automated tissue image analysis: Difference between revisions

Revision as of 00:49, 26 May 2016

Revision as of 02:00, 6 July 2016

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{{cite journal

|title=Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system

|authorauthors=Teverovskiy, M.; Kumar, V.; Junshui Ma; Kotsianti, A.; Verbel, D.; Tabesh, A.; Ho-Yuen Pang; Vengrenyuk, Y.; Fogarasi, S.; Saidi, O.; Aureon Biosciences Corp., Yonkers, NY, USA

|journal=Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium

|date=2004-04-18

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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| authorauthors=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| pages=15–24| publisher=Springer| doi=10.1007/s00138-010-0275-y| issue=1}}</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}}