外科手术计算机辅助导航技术

外科手术计算机辅助导航即利用计算机图形图像技术对放射影像学资料进行处理 ,重建二维或三维的医学图像模型 ,同时结合各种空间定位技术 ,在医师的双眼、手术工具及患者的头部之间建立一个实时的环路 ,实现手术过程中器械位置的实时或准实时显示。我们综述了外科手术计算机辅助导航系统的发展历史和研究现状 (重点阐述了其系统结构和关键技术 ,包括空间定位技术、图像处理与显示技术、系统配准技术、头部定位技术等 (最后给出了手术导航系统的发展趋势

双模式相关技术研究与实现

文中研究的相关跟踪技术主要应用于飞航导弹的末制导。现阶段，激光制导技术、GPS制导技术、合成孔径雷达（SAR）制导技术在我国的实际应用还不成熟，传统的电视、红外制导技术仍然具有很强的生命力，因而，基于可见光的视频相关跟踪技术具有重要的研究价值。本文主要论述的两种相关算法是：多灰度点相关（MPC）、区域模板相关（RTC）。其中多点相关（MPC）算法的跟踪灵敏度高，定位精度好，硬件实现比较方便，实时性能好；区域模板相关算法（RTC），在图像的匹配过程中，不仅考虑了目标区域的灰度特征，而且兼顾了区域里多灰度层次的位置特征、面积特征，算法具有很好的鲁棒性。文中深入研究了两种相关跟踪算法，并针对它们在实际应用中的不足，提出了有效的改善措施。最后，本文对两种相关跟踪算法进行了初步融合，一是：通过粗匹配、精匹配过程来选取目标跟踪点；二是：提出了一种度量模板更新的能量准则函数。大量的仿真实验结果表明：改进后的两种相关跟踪技术可以较好地完成一些复杂背景下的目标跟踪任务，两种算法的有效结合又进一步提高了目标跟踪的稳定性能和可靠性能。本文研究的一些相关跟踪技术已经运用到实际工程项目中。

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Image reconstruction by an alternating minimisation

This paper focuses on the problem of incomplete data in the applications of the circular cone-beam computed tomography. This problem is frequently encountered in medical imaging sciences and some other industrial imaging systems. For example, it is crucial when the high density region of objects can only be penetrated by X-rays in a limited angular range. As the projection data are only available in an angular range, the above mentioned incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. This paper reports a modified total variation minimisation method to reduce the data insufficiency in tomographic imaging. This proposed method is robust and efficient in the task of reconstruction by showing the convergence of the alternating minimisation method. The results demonstrate that this new reconstruction method brings reasonable performance. (C) 2010 Elsevier B.V. All rights reserved.