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Perspectives on combining Nonlinear Laser Scanning Microscopy and Bag-of-Features data classification strategies for automated disease diagnostics


Nonlinear Laser Scanning Microscopy (NLSM) techniques have been demonstrated in the past two decades as powerful imaging tools for disease diagnostics (DD). Currently, in most DD related experiments the interpretation of NLSM data sets is performed by trained specialists. Such approaches are both time consuming and prone to errors due to inter- and intra-observer discrepancies. The Bag-of-Features (BoF) paradigm has demonstrated its potential usefulness with respect to automated data classification in the frame of multiple experiments, but its intersections with the field of NLSM are at this moment scarce, to say the least. In this paper we review recent progress on DD using NLSM, and discuss necessary steps and potential future perspectives for merging NLSM and BoF to achieve complex frameworks for automated DD with high sensitivity and specificity.

© Springer Science+Business Media New York 2016
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