Medical Information Share of Medical Resure for Emergency Medical Assistance Team in Public Emergency

应急医学救援分队集合了拥有不同专业，在不同岗位进行临床作业的医疗救护人员，其在突发公共事件中担负着重要的任务，是应急医疗救援的关键保障力量，担负着前线急救，早期治疗，分类后送，指挥预警等重要任务，在前方和后方的指挥中心起着承上启下的关键作用。 利用先进的信息化技术和通讯手段，将信息技术与医疗技术进行有效整合，使得区域内的各医疗节点互相连通，实现伤（病）员基本信息及各处的诊疗信息的实时共享，为各医疗节点的协同合作提供保障基础。应急医学救援分队可以获取伤（病）员在区域内各医疗机构的既往诊疗数据，为医疗救治活动提供数据支持；区域内各医疗救治机构互联互通，提高了区域内医疗救援的协同合作能力；救援分队...Emergency medical assistance team (EMAT) plays an important role in medical rescue when public emergency occurred. It takes the task of first aid, early treatment, classification, command, early warning and transfer. It connects the front of incident with the rear of the base. With the help of information technology, we can build a regional health information share platform, with which medical es...学位：工程硕士院系专业：信息科学与技术学院_计算机技术学号：2302010115301

Fast local binary fitting optimization approach for image segmentation

针对灰度不均匀图像难以正确分割和分割结果依赖于初始轮廓的问题,提出一种快速稳定的分割算法,首先通过自适应距离保持水平集演化(AdPlS)算法进行初始分割以获取较好的初始轮廓,然后采用局部二值拟合(lbf)模型进行快速分割。实验表明,改进后的模型有良好的分割效果,较好地解决了分割速度、精度及稳定性之间的矛盾。It is difficult to get the correct segmentation results for the intensity inhomogeneity images,and the segmentation results are very sensitive to the initial contours.Thus,a fast and stable approach was proposed to overcome these disadvantages.First,an Adaptive Distance Preserving Level Set(ADPLS) method was utilized to get a better initial contour.Second,the Local Binary Fitting(LBF) model was used for a further segmentation.The experimental results show that the improved model can achieve good performance and is better to solve the contradiction among the segmentation speed,accuracy and stability.南京军区重点项目(11Z023); 福建省自然科学基金资助项目(2008J0312

Improved Method for Fuzzy C-Means Clustering Algorithm

由于传统的模糊C均值(fuzzy C-MEAnS,fCM)算法没有考虑像素点的空间邻域信息,仅涉及像素的单点灰度,在处理含有噪声的图像时有很大的局限性,因此分割效果较差。针对fCM的缺陷,提出一种新的改进算法,该算法引入gIbbS随机场,将gIbbS随机场先验概率与像素点隶属度的乘积作为新的像素隶属度。实验表明,改进后的算法有良好的分割效果,既可以较为完整地保留图像边界细节,又能较好地去除图像的噪声。The traditional fuzzy C-means (FCM) algorithm has great limitations in dealing with the noisy images owing to not considering the spatial information of the pixels and only involving the pixel gray of a single point,so it's poor in segmenting an image.For the defects of the FCM algorithm,a new improved algorithm is proposed in this article,in which a product of Gibbs priori probability and the membership is regarded as the new pixel membership.Experimental results show that the improved algorithm has a good segmentation result;it can retain more complete edge details of image and can remove the image noise more effectively.南京军区重点项目(项目编号:11Z023); 福建省自然科学基金项目(项目编号:2008J0312

Improvement of Segmentation Correction Model Based on Local Features

基于局部特性的分割校正模型能够在图像分割的同时实现对非均匀场的校正,从而对灰度不均匀图像有较好的分割效果,然而,该模型具有局部特性,且采用多相分割,使得活动轮廓曲线对初始位置较为敏感,且分割速度较慢。针对该不足,通过引入自适应距离保持水平集算法,采用分割校正模型与自适应距离保持水平集相结合的方法,提出一种新的快速分割算法。实验结果表明,该算法可摆脱初始轮廓的限制,避免在分割灰度不均匀图像时边缘泄漏和分割不足现象,并且具有分割快速的特点。Segmentation correction model based on local features can implement the correction of intensity inhomogeneity images,for which has better segmentation effect,but with the local features of the proposed model,and using multiphase segmentation,so that it is sensitive to the initial position of the active contour curve and the segmentation is slow.Aiming at the shortage,a new fast segmentation algorithm is proposed by introducing Adaptive Distance Preserving Level Set(ADPLS) algorithm,combining the segmentation correction model and adaptive distance preserving level set method.Experimental results show that the improved algorithm not only gets rid of the influence of the initial contour,but also avoids the edge leakage and segmentation shortage phenomenon,maintains the fast segmentation.福建省自然科学基金资助项目(2008J0312