Sensitivity study of a semiautomatic supervised classifier applied to minerals from xray mapping images 
Rasmus Larsen, Allan Aasbjerg Nielsen, Harald Flesche

Abstract  This paper addresses the problem of assessing the robustness with
respect to change in parameters of an integrated training and
classification routine for minerals commonly encountered in
siliciclastic or carbonate rocks.
Twelve chemical elements are mapped from thin sections by energy dispersive
spectroscopy (EDS) in a scanning electron microscope (SEM).
Extensions to traditional multivariate
statistical methods are applied to perform the classification.
Training sets are grown from one or a few seed points
by a method that ensures spatial and spectral closeness of observations.
Spectral closeness is obtained by excluding observations that have high
Mahalanobis distances to the training class mean.
Spatial closeness is obtained by requiring connectivity.
The marginal effects of changes in
the parameters that are input to the seed growing algorithm
are evaluated. Initially,
the seed is expanded to a small area in order to allow for the estimation of
a variancecovariance matrix. This expansion is controlled by upper limits
for the spatial and Euclidean
spectral distances from the seed point. Second, after
this initial expansion the growing of the training set is controlled by
an upper limit for the Mahalanobis distance to the current estimate of the
class centre. Also, the estimates of class centres and covariance matrices may
be continuously updated or the initial estimates may be used. Finally,
the effect of the
operator's choice of seed among a number of potential seeding points
is evaluated.
After training, a standard quadratic classifier is applied. The
performance for each parameter setting is measured by the overall
misclassification rate on an independently generated validation set.
The classification
method is presently used as a routine petrographical analysis
method at Norsk Hydro Research Centre. 
Keywords  classification, region growing, seed, supervised 
Type  Journal paper [With referee] 
Journal  Pattern Recognition Letters 
Year  2000 Vol. 21 No. 1314 pp. 11751182 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Image Analysis & Computer Graphics 