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Classification of cervical cell nuclei using morphological segmentation and textural feature extraction

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4 Author(s)
Walker, R.F. ; Dept. of Elecr. & Comput. Eng., Queensland Univ., Brisbane, Qld., Australia ; Jackway, P. ; Lovell, B. ; Longstaff, I.D.

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

Published in:

Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on

Date of Conference:

29 Nov-2 Dec 1994

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