A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection

The objective of this paper is to develop a complete solution for recurrent nasal papilloma (RNP) detection. Recently, Gadolinium-enhanced dynamic magnetic resonance imaging (MRI) has been developed and widely used in the clinical diagnosis of RNP. Because the response of RNP regions in Gadolinium-enhanced MR images is different from the response of normal tissues, the difference between the dynamic-MR images before and after administering a contrast material can be used to extract coarse RNP regions automatically. In this study, a fuzzy algorithm for learning vector quantization neural network is used to pick suspicious RNP regions. Finally, a feature-based region growing method is applied to recover complete RNP regions. The experimental results show that the proposed method can detect RNP regions automatically, correctly, and fast.