Recurrent Nasal Papilloma Detection Using a Fuzzy Algorithm Learning Vector Quantization Neural Network

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 RNP regions. The experimental results show that the proposed method can detect RNP regions automatically, correctly and fast.