Department of Computer Science, ETH Zurich, Zurich, Switzerland,
J Digit Imaging. 2013 Oct;26(5):920-31. doi: 10.1007/s10278-013-9576-9.
Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.
在西方世界，克罗恩病（CD）的发病率不断上升，因此准确诊断该病是一项重要的医学挑战。目前的诊断参考标准——结肠镜检查既耗时又具侵入性，而磁共振成像（MRI）已成为优于结肠镜检查的首选非侵入性检查方法。目前的 MRI 方法评估对比增强和肠壁厚度的速度，并依靠广泛的手动分割进行准确分析。我们提出了一种监督学习方法，用于识别和定位腹部磁共振图像中受 CD 影响的区域。使用强度和纹理等低级特征以及形状不对称信息来区分患病和正常区域。特别强调一种新颖的基于熵的形状不对称方法以及偏度和峰度等高阶统计量。多尺度特征提取使该方法具有鲁棒性。对真实患者数据的实验表明，我们的特征具有很高的准确性，并且比两种竞争方法表现更好。
