脑图谱构建中的三维显微图像关节形变配准研究

项目名称： 脑图谱构建中的三维显微图像关节形变配准研究

项目编号： No.61201396

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 屈磊

作者单位： 安徽大学

项目金额： 25万元

中文摘要： 三维显微图像配准是构建模式生物大脑神经元图谱的关键使能技术之一，然而一些模式生物在特定发育阶段或部分神经系统中普遍存在的关节形变使得现有配准系统不再适用或不能取得满意的配准结果，这极大限制了神经元发育图谱的构建及相关的高通量神经环路系统化研究。 在前期研究中，我们将点集间匹配的聚类实现模型成功扩展到点集到图像的匹配，并证实，该聚类实现可在获得邻域及先验信息更加高效利用的同时极大提高匹配过程对初始化和噪声的鲁棒性。本申请在前期基础上进一步深化创新，将该聚类实现思想扩展到关节形变图像的全局及局部柔性配准问题，研究全局配准中关节形变模式到图像匹配的鲁棒聚类实现，探讨图像局部配准的像素/区域到像素/区域双向聚类实现机制，同时构建更加有效的关节形变描述模型，最终实现高效、鲁棒和准确的关节形变配准系统。该研究成果将为神经环路研究及脑发育图谱构建提供有力支撑，同时可能为一般性的图像配准开辟一条新路。

中文关键词： 图像配准；关节形变；神经元分类；稀疏表示；特征匹配

英文摘要： 3D microscopic image registration is one of the key enabling techniques in building digital brain atlases of model organisms. However, the articulated deformation which generally existed in the specific developmental stages or parts of the nerve system makes the current registration systems no longer applicable or cannot produce satisfactory registration results. This problem places a huge obstacle in the developmental neuronal atlases building and related high throughput neural circuit study. In our previous research, the clustering implementation of matching between point sets has been successfully extended to the matching between point set and image. The experimental results verified that the neighboring information and prior knowledge can be used in a more efficient way under the clustering framework. In addition, the robustness of iteration process to the initialization and noise can be greatly improved as well. This study encourages us to further extend the clustering implementation to the global and local non-rigid registration of articulated deformation. This proposal aims to study the robust clustering implementation of the matching between articulated deformation pattern and image in the global registration, and for the local registration, explore the two-way clustering mechanism between pixels/regions

英文关键词： Image registration；articulated deformation；neuron classification；sparse representation；feature matching