Structure analysis and classification of cervical cells using a processing system based on TV.
This paper presents preliminary results of a cell classification experiment using a new approach for feature extraction. The algorithm takes into account the special requirements of a fast parallel processing system (processor-oriented algorithms). A cell image is described by several hundred features derived from the nucleus only. The most significant features with respect to classification are determined by statistical analysis. Applying principal axis transform, a new feature set is computed, reduced considerably in dimensions. The data base (1,925 cell images of Papanicolaou-stained cervical specimens) was divided into a training set (963 images) and a test set (962 images). The classification results of the test set show that the recognition rate for the two-class problem (normal, suspicious) is better than 91%, using only ten morphologic features.
D002582:Cervix Mucus / Q000166:cytology*; D003584:Cytological Techniques; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008390:Markov Chains; D011336:Probability; D012044:Regression Analysis; D013690:Television; D013997:Time Factors