Luts Jan, Heerschap Arend, Suykens Johan A K, Van Huffel Sabine
Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SCD (SISTA), Kasteelpark Arenberg 10, B-3001 Leuven-Heverlee, Belgium.
Artif Intell Med. 2007 Jun;40(2):87-102. doi: 10.1016/j.artmed.2007.02.002. Epub 2007 Apr 26.
This study investigates the use of automated pattern recognition methods on magnetic resonance data with the ultimate goal to assist clinicians in the diagnosis of brain tumours. Recently, the combined use of magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) has demonstrated to improve the accuracy of classifiers. In this paper we extend previous work that only uses binary classifiers to assess the type and grade of a tumour to a multiclass classification system obtaining class probabilities. The important problem of input feature selection is also addressed.
Least squares support vector machines (LS-SVMs) with radial basis function kernel are applied and compared with linear discriminant analysis (LDA). Both a Bayesian framework and cross-validation are used to infer the parameters of the LS-SVM classifiers. Four different techniques to obtain multiclass probabilities as a measure of accuracy are compared. Four variable selection methods are explored. MRI and MRSI data are selected from the INTERPRET project database.
The results illustrate the significantly better performance of automatic relevance determination (ARD), in combination with LS-SVMs in a Bayesian framework and coupling of class probabilities, compared to classical LDA.
It is demonstrated that binary LS-SVMs can be extended to a multiclass classifier system obtaining class probabilities by Bayesian techniques and pairwise coupling. Feature selection based on ARD further improves the results. This classifier system can be of great help in the diagnosis of brain tumours.
本研究探讨自动模式识别方法在磁共振数据中的应用，最终目标是协助临床医生诊断脑肿瘤。最近，磁共振成像（MRI）和磁共振波谱成像（MRSI）的联合使用已证明可提高分类器的准确性。在本文中，我们将先前仅使用二元分类器评估肿瘤类型和分级的工作扩展到一个获得类别概率的多类分类系统。还解决了输入特征选择这一重要问题。
应用具有径向基函数核的最小二乘支持向量机（LS - SVM），并与线性判别分析（LDA）进行比较。使用贝叶斯框架和交叉验证来推断LS - SVM分类器的参数。比较了四种不同的获取多类概率作为准确性度量的技术。探索了四种变量选择方法。MRI和MRSI数据选自INTERPRET项目数据库。
结果表明，与经典的LDA相比，在贝叶斯框架下将自动相关性确定（ARD）与LS - SVM相结合并耦合类别概率，性能有显著提升。
结果表明，二元LS - SVM可通过贝叶斯技术和成对耦合扩展为一个获得类别概率的多类分类器系统。基于ARD的特征选择进一步改善了结果。该分类器系统对脑肿瘤的诊断有很大帮助。
