A Web Simulation of Medical Image Reconstruction and Processing as an Educational Tool

Web educational resources integrating interactive simulation tools provide students with an in-depth understanding of the medical imaging process. The aim of this work was the development of a purely Web-based, open access, interactive application, as an ancillary learning tool in graduate and postgraduate medical imaging education, including a systematic evaluation of learning effectiveness. The pedagogic content of the educational Web portal was designed to cover the basic concepts of medical imaging reconstruction and processing, through the use of active learning and motivation, including learning simulations that closely resemble actual tomographic imaging systems. The user can implement image reconstruction and processing algorithms under a single user interface and manipulate various factors to understand the impact on image appearance. A questionnaire for pre- and post-training self-assessment was developed and integrated in the online application. The developed Web-based educational application introduces the trainee in the basic concepts of imaging through textual and graphical information and proceeds with a learning-by-doing approach. Trainees are encouraged to participate in a pre- and post-training questionnaire to assess their knowledge gain. An initial feedback from a group of graduate medical students showed that the developed course was considered as effective and well structured. An e-learning application on medical imaging integrating interactive simulation tools was developed and assessed in our institution.     

基于功能导向-算法集成的西洋参双功能保健食品组方规律和设计研究

目的 基于西洋参“保健食品原料”与“中药材”的双重身份，以功能为导向，融合算法分析，实现西洋参保健食品组方规律挖掘与组方设计研究。方法 依据功能导向-算法集成，收集西洋参保健食品数据与临床应用数据，借助BDP平台、自然语言处理与信息检索（natural language processing&information retrieval sharing platform,NLPIR）共享平台实现数据处理与信息挖掘，综合分析具有潜在开发价值的保健食品功能；以选定功能为导向，收集西洋参保健食品、中成药、方剂的组方信息并建立数据库，统计高频药味及类别，采用Apriori关联规则算法、层次分析法（analytic hierarchy process,AHP）-熵权-自组织特征映射（self-organizing maps,SOM）联用的赋权聚类算法（主观与客观算法联用）、逼近理想解排序（technique for order preference by similarity to an ideal solution,TOPSIS）综合评价法等机器学习算法提取样本数据特征并分析总结其内在规...     

Predictors of perioperative complications in paediatric cranial vault reconstruction surgery: a multicentre observational study from the Pediatric Craniofacial Collaborative Group

Background

The current incidence of major complications in paediatric craniofacial surgery in North America has not been accurately defined. In this report, the Pediatric Craniofacial Collaborative Group evaluates the incidence and determines the independent predictors of major perioperative complications using a multicentre database.

A Tongue-images Segmentation Method Based on Local Restoration and Watershed Algorithm

Objective: To propose a method to segment tongue-images efficiently, and extract tongue-body accurately and quickly. Methods: Firstly, a kind of color-images' pre-processing technique was used to solve tongue-surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue-surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation-dilation to get a closed-loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region-combination technique was used. Through measuring neighbourship and similarity of regions, a non-objective and non-background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue-body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue-body image from its original one effectively, and thus laying a good foundation for the following analysis work.