Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Introduction to algorithms
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
An automated cervical pre-cancerous diagnostic system
Artificial Intelligence in Medicine
Particle swarm optimization for pap-smear diagnosis
Expert Systems with Applications: An International Journal
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
IEEE Transactions on Information Technology in Biomedicine
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In this work, we present a framework for the efficient classification of cervical cells in normal and abnormal categories, based on features extracted exclusively from the nucleus area and ignoring the contingent cytoplasm features. This task is very important, since the nuclei are the only distinguishable areas in complex Pap smear images, as these images present a high degree of cell overlapping and the exact borders of the cytoplasm areas are ambiguous. We have examined the ability of non-linear dimensionality reduction schemes to produce accurate representation of the features manifold, along with the definition of an efficient feature subset, and their influence on the classification performance. Two unsupervised classifiers were used and the results indicate that we can achieve high classification performance when only the nuclei features are used.