Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction

This paper presents preliminary results for the classification of Pap smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textual 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 preformed 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