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Cell image classification using histograms, higher order statistics and adaboost

By Vinod Chandran, Jasmine Banks, Wageeh Boles, Brenden Chen and Inmaculada Tomeo-Reyes

Abstract

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

Topics: Cell, Classification, Histogram, Bispectrum, Higher order statistics, Adaboost
Publisher: IEEE
Year: 2013
OAI identifier: oai:eprints.qut.edu.au:68693
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