Lahmiri Salim, Boukadoum Mounir
Department of Computer Science, University of Quebec at Montreal, 201 President-Kennedy, Local PK-4150, Montreal, QC, Canada H2X 3Y7.
ISRN Radiol. 2013 Oct 29;2013:627303. doi: 10.5402/2013/627303. eCollection 2013.
We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer's disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 98.20% ± 0.02 specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD.
我们提出了一种用于检测受阿尔茨海默病（AD）影响的脑磁共振图像（MRI）的新型自动化系统。通过多尺度分析（MSA）对MRI进行分析，以在六个不同尺度上获得其分形。提取的分形用作特征，通过支持向量机（SVM）分类器区分健康脑MRI和AD脑MRI。使用留一法交叉验证方法对93幅脑MRI（包括51幅健康脑图像和42幅受AD影响的脑图像）进行分类的结果，得出分类准确率为99.18%±0.01，灵敏度为100%，特异性为98.20%±0.02。这些结果以及5.64秒的处理时间表明，所提出的方法可能是放射科医生在AD筛查中一种有效的诊断辅助手段。
