Automatic recognition of biological particles in microscopic images Export

@article{citeulike:4234766, abstract = {A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2\% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83\% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good.}, author = {Ranzato, M. and Taylor, P. and House, J. and Flagan, R. and Lecun, Y. and Perona, P.}, citeulike-article-id = {4234766}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.patrec.2006.06.010}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0167865506001668}, day = {01}, doi = {10.1016/j.patrec.2006.06.010}, issn = {01678655}, journal = {Pattern Recognition Letters}, keywords = {biology, classification, microscopy}, month = {January}, number = {1}, pages = {31--39}, posted-at = {2009-03-29 04:22:30}, priority = {2}, title = {Automatic recognition of biological particles in microscopic images}, url = {http://dx.doi.org/10.1016/j.patrec.2006.06.010}, volume = {28}, year = {2007} }

TY - JOUR ID - citeulike:4234766 L3 - citeulike-article-id:4234766 N2 - A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good. IS - 1 JF - Pattern Recognition Letters SN - 01678655 EP - 39 TI - Automatic recognition of biological particles in microscopic images VL - 28 SP - 31 KW - biology KW - classification KW - microscopy AU - Ranzato, M AU - Taylor, P AU - House, J AU - Flagan, R AU - Lecun, Y AU - Perona, P PY - 2007/01/01/ UR - http://dx.doi.org/10.1016/j.patrec.2006.06.010 ER -