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首页> 外文期刊>Biomedical signal processing and control >Cirrhosis classification based on MRI with duplicative-feature support vector machine (DFSVM)
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Cirrhosis classification based on MRI with duplicative-feature support vector machine (DFSVM)

机译：基于MRI的肝硬化分类特征支持向量机（DFSVM）分类

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摘要

Magnetic resonance imaging (MRI) is a sensitive diagnostic method in improving the diagnostic capacity for hepatic cirrhosis and determining the accurate characterization of hepatic cirrhosis. But hepatic MRI has some shortcomings in detection and classification hepatic cirrhosis in clinical, especially using non-enhanced MRI for diagnosing early hepatic cirrhosis. And computer-aided diagnostic (CAD) system, including quantitative description of lesion and automatically classification, can provide radiologists or physicians an alternative second opinion to efficiently apply the abundant information of the hepatic MRI. However, it is expected to character comprehensively the lesion and guarantee high classification rate of CAD system. In this paper, a new CAD system for hepatic cirrhosis detection and classification from normal hepatic tissue non-enhanced MRI is presented. According to prior approach, six texture features with different properties based on gray level difference method are extracted from regions of interest (ROI). Then duplicative-feature support vector machine (DFSVM) is proposed for feature selection and classification: Firstly, the search process of DFSVM imitates diagnosis of doctors: doctor will take a more feature for consideration until the final diagnoses regardless of whether the feature is used in advance. So our algorithm is consistent with the process of clinical diagnosis. Secondly, the impact of the most valuable features will be well strengthened and then the high prediction performance can be got. Experimental results also illustrate the satisfying classification rate. Performance of extracted features and normalization are studied. And it is also compared with typical classifier ANN.
机译：磁共振成像（MRI）是提高肝硬化诊断能力并确定肝硬化准确特征的灵敏诊断方法。但是肝MRI在临床上对肝硬化的检测和分类有一定的不足，特别是采用非增强MRI对早期肝硬化进行诊断。计算机辅助诊断（CAD）系统（包括病变的定量描述和自动分类）可以为放射科医生或医生提供替代的第二意见，以有效地应用肝脏MRI的大量信息。但是，期望能够对病变进行综合表征，并保证CAD系统的高分类率。本文提出了一种新的CAD系统，用于从正常肝组织非增强MRI进行肝硬化的检测和分类。根据现有方法，从关注区域（ROI）提取基于灰度差异法的具有不同属性的六个纹理特征。然后提出了复制特征支持向量机（DFSVM）用于特征选择和分类：首先，DFSVM的搜索过程模仿了医生的诊断：在最终诊断之前，医生将考虑更多特征，无论该特征是否用于预先。因此我们的算法与临床诊断过程是一致的。其次，最有价值的特征的影响将得到充分增强，然后可以获得较高的预测性能。实验结果也说明了令人满意的分类率。研究了提取特征的性能和规范化。并将其与典型分类器ANN进行比较。

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