Recurrent nasal papilloma detection using a fuzzy algorithm learning vector quantization neural network

被引：0
|
作者：
Chuan-Yu Chang [1 ]
Da-Feng Zhuang [1 ]
机构：
[1] Natl Yunlin Univ Sci & Technol, Dept Elect Engn, Yunlin, Taiwan
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS | 2006年
关键词：
D O I：
10.1109/ICSMC.2006.384443
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The objective of this paper is to develop a complete solution for recurrent nasal papilloma (RNP) detection. Recently, the Gadolinium-enhanced dynamic magnetic resonance image (MRI) has been developed and widely used in clinical diagnosis of recurrent nasal papilloma. Owing to the response of RNP regions in Gadolinium-enhanced magnetic resonance images is different from the response of normal tissues, the difference between the dynamic-MR images before and after administering contrast material can be used to extract the coarse RNP regions automatically. Then, a fuzzy algorithm for learning vector quantization (FALVQ) neural network is used to pick the suspicious RNP regions. Finally, a feature-based region growing method is applied to recover the complete R_NP regions. The experimental results show that the proposed method can detect RNP regions automatically, correctly and fast.
引用
收藏
页码：556 / +
页数：2
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