ABSTRACT
This paper presents preliminary results for the
classification of Pap Smear cell nuclei, using Gray Level
Co-occurrence Matrix (GLCM) textural features. We outline a method of
nuclear segmentation using fast morphological gray-scale
transforms. For each segmented nucleus, features derived from a
modified form of the GLCM are extracted over several angle and
distance measures. Linear Discriminant Analysis is performed on these
features to reduce the dimensionality of the feature space, and a
classifier with hyper-quadric decision surface is implemented to
classify a small set of normal and abnormal cell nuclei. Using 2
features, we achieve a misclassification rate of 3.3\% on a data set
of 61 cells.}
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