CLASSIFICATION OF CERVICAL CELL NUCLEI USING MORPHOLOGICAL SEGMENTATION AND TEXTURAL FEATURE EXTRACTIONy

This paper presents preliminary results for the classiication of Pap Smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textural features. We outline a method of nuclear segment-ation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modiied 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 classiier with hyper-quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassiication rate of 3.3% on a data set of 61 cells. This paper presents preliminary results for the classiication of Pap Smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textural features. We outline a method of nuclear segment-ation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modiied 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 classiier with hyper-quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassiication rate of 3.3% on a data set of 61 cells.