Contour features for colposcopic image classification by artificial neural networks

被引：0

作者：

Claude, I ^{[1
]}

Winzenrieth, R ^{[1
]}

Pouletaut, P ^{[1
]}

Boulanger, JC ^{[1
]}

机构：

[1] Univ Technol Compiegne, F-60206 Compiegne, France

来源：

16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS | 2002年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article 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. Afore 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 contour image set has been correctly, classified.

引用

页码：771 / 774

页数：4

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