Contour features for colposcopic image classification by artificial neural networks

Abstract

Presents colposcopic image classification based on contour parameters used in a comparison study of different artificial neural networks and the k-nearest neighbors reference method. In this study, significant image data bases are used (283 samples) from which a set of original parameters is extracted to characterize the attribute of contour. More precisely, we quantify the notion of sharp contours vs. blurred contours in computing spatial parameters based on the number of small regions near boundaries of objects and frequency parameters based on power spectrum of lines cutting these boundaries. Experimental results show the feasibility of this study and the efficiency of the set of parameters since 95.8% of the contour image set has been correctly classified.