Abdullah Bassem A, Younis Akmal A, John Nigel M
Department of Electrical and Computer Engineering, University of Miami, Miami, Fl, USA.
Open Biomed Eng J. 2012;6:56-72. doi: 10.2174/1874230001206010056. Epub 2012 May 9.
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
本文提出了一种从脑磁共振成像（MRI）数据中自动分割多发性硬化（MS）病变的新技术。该技术使用经过训练的支持向量机（SVM），主要基于纹理特征并借助其他特征来区分MS病变区域中的块和非MS病变区域中的块。分类在脑的轴向、矢状和冠状截面视图上分别独立进行，然后将所得分割结果汇总以提供更准确的输出分割。本文所述的这项新技术的主要贡献在于，采用纹理特征以完全自动化的方式检测MS病变，且不依赖于手动描绘MS病变。此外，该技术引入了多截面视图分割的概念以生成经过验证的分割。使用三个模拟数据集和五十多个真实MRI数据集对所提出的基于纹理的SVM技术进行了评估。将结果与现有方法进行了比较。所得结果表明，所提出的方法在临床实践中用于检测MRI中的MS病变是可行的。
