Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images
Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images
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DOI：
10.1109/titb.2010.2091279
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发表时间：
2011-01-01
影响因子：
--
通讯作者：
Pattichis, C. S.
中科院分区：
文献类型：
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作者：
Loizou, C. P.;Murray, V.;Pattichis, C. S.
本研究介绍了使用多尺度调幅-调频（AM-FM）纹理分析多发性硬化症（MS）的磁共振（MR）图像从大脑。在临床上，有兴趣确定病变纹理和疾病进展之间的潜在关联，并在相关的纹理特征与相关的临床指标，如扩展残疾状态量表（EDSS）。这项纵向研究探讨了应用2-D AM-FM分析脑白色物质MS病变，以量化和监测疾病负荷。为此，MS病变和正常外观的白色物质（NAWM）从MS患者，以及正常的白色物质（NWM）从健康志愿者，分割横向T2加权图像从连续脑MR成像（MRI）扫描（0和6-12个月）。在不同尺度下，从每个分割区域提取瞬时幅度（IA）、瞬时频率（IF）的幅度和IF角度。结果表明，AM-FM特征成功区分1）NWM和病变; 2）NAWM和病变; 3）NWM和NAWM。支持向量机（SVM）分类器成功区分患者，在初始MRI扫描后两年内，获得EDSS 2（正确分类率= 86%）。低尺度IA和IF震级与中尺度IA的组合获得了最好的分类结果。的AM-FM功能提供了补充信息，经典的纹理分析功能，如灰度中值，对比度和粗糙度。本研究结果提供的证据表明，AM-FM功能可能具有作为MS病变负荷替代标记物的潜在作用。
This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from brain. Clinically, there is interest in identifying potential associations between lesion texture and disease progression, and in relating texture features with relevant clinical indexes, such as the expanded disability status scale (EDSS). This longitudinal study explores the application of 2-D AM-FM analysis of brain white matter MS lesions to quantify and monitor disease load. To this end, MS lesions and normal-appearing white matter (NAWM) from MS patients, as well as normal white matter (NWM) from healthy volunteers, were segmented on transverse T2-weighted images obtained from serial brain MR imaging (MRI) scans (0 and 6-12 months). The instantaneous amplitude (IA), the magnitude of the instantaneous frequency (IF), and the IF angle were extracted from each segmented region at different scales. The findings suggest that AM-FM characteristics succeed in differentiating 1) between NWM and lesions; 2) between NAWM and lesions; and 3) between NWM and NAWM. A support vector machine (SVM) classifier succeeded in differentiating between patients that, two years after the initial MRI scan, acquired an EDSS 2 (correct classification rate = 86%). The best classification results were obtained from including the combination of the low-scale IA and IF magnitude with the medium-scale IA. The AM-FM features provide complementary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. The findings of this study provide evidence that AM-FM features may have a potential role as surrogate markers of lesion load in MS.