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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Artificial intelligencePattern recognition (psychology)SegmentationFeature extractionComputer scienceLinear discriminant analysisCurse of dimensionalityClassifier (UML)DiscriminantGray levelData setImage (mathematics)
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 297-301
- Citations
- 37
- Access
- Closed
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Cite This
Ross F. Walker,
Paul Jackway,
Brian C. Lovell
et al.
(2002).
Classification of cervical cell nuclei using morphological segmentation and textural feature extraction.
, 297-301.
https://doi.org/10.1109/anziis.1994.396977
Identifiers
- DOI
- 10.1109/anziis.1994.396977