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首页> 外文期刊>BioTechnology: An Indian Journal >Nasopharynx segmentation in MR images based on one-class immune feature weighted support vector machines
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Nasopharynx segmentation in MR images based on one-class immune feature weighted support vector machines

机译：基于一类免疫特征加权支持向量机的MR图像鼻咽分割

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In the brain Magnetic Resonance (MR) images, the nasopharynx part is highly irregular. It is difficult to accurately segment this part. Owing to its powerful capacity in solving non-linearity problems, One-class Support Vector Machine (SVM) method has been widely used as a segmentation tool. However, the conventional one-class SVMs assume that each feature of the samples has the same importance degree for the segmentation result, which is not necessarily true in real applications. In addition, oneclass SVM parameters also affect the segmentation result. In this study, ImmuneAlgorithm(IA)was introduced in searching for the optimal feature weights and the parameters simultaneously.An Immune FeatureWeighted SVM (IFWSVM) method was used to segment the nasopharynx in MR images. Theoretical analysis and experimental results showed that the IFWSVM had better performance than the conventional methods.
机译：在大脑磁共振（MR）图像中，鼻咽部分高度不规则。很难准确地分割这部分。由于其解决非线性问题的强大能力，一类支持向量机（SVM）方法已被广泛用作分割工具。然而，常规的一类SVM假设样本的每个特征对于分割结果具有相同的重要度，这在实际应用中不一定是正确的。此外，oneclass SVM参数也会影响分割结果。在这项研究中，引入了ImmuneAlgorithm（IA）来同时搜索最佳特征权重和参数。采用免疫特征加权SVM（IFWSVM）方法对MR图像中的鼻咽进行分割。理论分析和实验结果表明，IFWSVM具有比常规方法更好的性能。

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