Cell Image Classification Using Histograms, Higher Order Statistics and Adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over predefined 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 [1]. 108 features were considered as candidates for Adaboost [2] 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.