Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme
Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme
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DOI：
10.1016/j.cmpb.2008.01.006
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发表时间：
2008-06-01
影响因子：
6.1
通讯作者：
Cavouras, Dionisis
中科院分区：
文献类型：
--
作者：
Glotsos, Dimitris;Kalatzis, Loannis;Cavouras, Dionisis
星形细胞瘤的分级是治疗计划的一项重要任务;然而，它受到观察者之间显著的差异性的影响。已经提出计算机辅助诊断系统来帮助最小化主观性，然而，这些系统呈现中等准确性或利用难以在日常临床实践中应用的专门染色方案和分级系统。本研究提出了一个强大的数学公式，通过整合国家的最先进的技术（支持向量机和最小二乘映射）在级联分类方案，从高和III级星形细胞肿瘤IV级分离低。结果表明，低级别肿瘤与高级别肿瘤的正确区分率高达97.3%，而III级肿瘤与IV级肿瘤的正确区分率为97.8%。整体表现为95.2%。这些高比率是在分类之前对特征应用最小二乘映射技术的结果。最小二乘映射的一个重要副产品是SVM分类器的支持向量的数量从不使用映射时的约80%急剧下降到使用映射时的不到5%。后者清楚地表明，SVM分类器具有更大的潜力，可以很好地推广到新数据。通过这种方式，用于星形细胞瘤自动分级的数字图像分析系统更接近临床实践。(c)2008爱思唯尔爱尔兰有限公司保留所有权利。
Grading of astrocytomas is an important task for treatment planning; however, it suffers from significantly great inter-observer variability. Computer-assisted diagnosis systems have been propose to assist towards minimizing subjectivity however, these systems present either moderate accuracy or utilize specialized staining protocols and grading systems that are difficult to apply in daily clinical practice. The present study proposes a robust mathematical formulation by integrating state-of-art technologies (support vector machines and least squares mapping) in a cascade classification scheme for separating low from high and grade III from grade IV astrocytic tumours. Results have indicated that low from high-grade tumours can be correctly separated with a certainty as high as 97.3%, whereas grade III from grade IV tumours with 97.8%. The overall performance was 95.2%. These high rates have been a result of applying the least squares mapping technique to features prior to classification. A significant byproduct of least squares mapping is that the number of support vectors of the SVM classifiers dropped dramatically from about 80% when no mapping was used to less than 5% when mapping was used. The latter is a clear indication that the SVM classifier has a greater potential to generalize well to new data. In this way, digital image analysis systems for automated grading of astrocytomas are brought closer to clinical practice. (c) 2008 Elsevier Ireland Ltd. All rights reserved.