Castellani Umberto, Mirtuono Pasquale, Murino Vittorio, Bellani Marcella, Rambaldelli Gianluca, Tansella Michele, Brambilla Paolo
VIPS lab, University of Verona, Italy.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):426-33. doi: 10.1007/978-3-642-23629-7_52.
In this paper, we exploit spectral shape analysis techniques to detect brain morphological abnormalities. We propose a new shape descriptor able to encode morphometric properties of a brain image or region using diffusion geometry techniques based on the local Heat Kernel. Using this approach, it is possible to design a versatile signature, employed in this case to classify between normal subjects and patients affected by schizophrenia. Several diffusion strategies are assessed to verify the robustness of the proposed descriptor under different deformation variations. A dataset consisting of MRI scans from 30 patients and 30 control subjects is utilized to test the proposed approach, which achieves promising classification accuracies, up to 83.33%. This constitutes a drastic improvement in comparison with other shape description techniques.
在本文中，我们利用光谱形状分析技术来检测脑部形态异常。我们提出了一种新的形状描述符，它能够使用基于局部热核的扩散几何技术对脑图像或区域的形态计量学特性进行编码。使用这种方法，可以设计一种通用的特征，在这种情况下用于对正常受试者和精神分裂症患者进行分类。评估了几种扩散策略，以验证所提出的描述符在不同变形变化下的鲁棒性。利用一个由30名患者和30名对照受试者的MRI扫描组成的数据集来测试所提出的方法，该方法取得了高达83.33%的可观分类准确率。与其他形状描述技术相比，这有了显著的改进。
