Phytoplankton recognition using parametric discriminants

Abstract

A comparison was made between the use of linear and quadratic discriminant functions for classifying phytoplankton specimens of the genera Dinophysis and Ceratium by means of a general morphometric function. The class distributions were found to fit quadratic boundaries better than linear boundaries. A nine species quadratic discriminant classified within 95% confidence intervals. Morphological variants not used in the calibration were all correctly identified, although control species unknown to the model were poorly rejected. An accuracy of 99% was obtained for separating three morphological variants of Dinophysis acuminata. Digital filters were developed to extract the morphometric function directly from photomicrograph images, and present the data as an orientation-independent feature vector. Using this feature vector, a quadratic discriminant classified test data from 14 species of the genera Dinophysis, Ceratium and Ornithocercus with an accuracy of 83%, with 37% of the error due to two similarly shaped species of Dinophysis overlapping.