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首页> 外文期刊>IEEE transactions on circuits and systems . I , Regular papers >A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection
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A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection

机译：基于模糊的学习矢量量化神经网络用于复发性鼻乳头瘤的检测

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摘要

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.
机译：本文的目的是为复发性鼻乳头状瘤（RNP）检测开发一个完整的解决方案。近年来，Ga增强动态磁共振成像（MRI）已被开发并广泛用于RNP的临床诊断。由于Ga增强的MR图像中RNP区域的响应与正常组织的响应不同，因此在使用对比剂之前和之后，动态MR图像之间的差异可用于自动提取粗糙的RNP区域。在这项研究中，一种用于学习矢量量化神经网络的模糊算法被用于选择可疑的RNP区域。最后，基于特征的区域增长方法被应用于恢复完整的RNP区域。实验结果表明，该方法能够自动，正确，快速地检测出RNP区域。

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