Cell image classification using histograms, higher order statistics and adaboost
Chandran, Vinod, Banks, Jasmine, Boles, Wageeh, Chen, Brenden, & Tomeo-Reyes, Inmaculada (2013) Cell image classification using histograms, higher order statistics and adaboost. In The 20th IEEE International Conference on Image Processing (ICIP) - International Competition on Cells Classification by Fluorescent Image Analysis, 15 - 18 September 2013, Melbourne, Australia.
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A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.
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|Item Type:||Conference Item (Presentation)|
|Keywords:||Cell, Classification, Histogram, Bispectrum, Higher order statistics, Adaboost|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 IEEE|
|Deposited On:||17 Mar 2014 23:37|
|Last Modified:||29 Apr 2014 06:12|
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