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首页> 外文期刊>Bulletin of the Polish Academy of Sciences. Technical Sciences >Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis
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Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

机译：后曲韧带计算机辅助诊断所选分类剂的比较分析

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摘要

A study on computer aided diagnosis of posterior cruciate ligaments is presented in this paper. The diagnosis relies on T1-weighted magnetic resonance imaging. During the image analysis stage, the ligament region is automatically detected, localized, and extracted using fuzzy segmentation methods. Eight geometric features are defined for the ligament object. With a clinical reference database containing 107 cases of both healthy and pathological cases, a Fisher linear discriminant is used to select 4 most distinctive features. At the classification stage we employ five different soft computing classifiers to evaluate the feature vector suitability for the computerized ligament diagnosis. Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems. The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the computer aided ligaments diagnosis.
机译：本文对后交叉韧带的计算机辅助诊断进行了研究。诊断依靠T1加权磁共振成像。在图像分析阶段，使用模糊分割方法自动检测、定位和提取韧带区域。为韧带对象定义了八个几何特征。在包含107例健康和病理病例的临床参考数据库中，使用Fisher线性判别法来选择4个最显著的特征。在分类阶段，我们使用五种不同的软计算分类器来评估特征向量对计算机韧带诊断的适用性。在这些分类器中，我们介绍并指定了基于粒子群优化的Sugeno型模糊推理系统，并将其性能与其他已建立的分类系统进行了比较。每个分类器的分类准确度指标：灵敏度、特异性和Dice指数均超过90%，这表明在计算机辅助韧带诊断中提出的特征向量相关性水平较高。

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