Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images

Modern endoscopes play a significant role in diagnosing various gastrointestinal (GI) tract related diseases where the visual quality of endoscopic images helps improving the diagnosis. This article presents an image enhancement method for color endoscopic images that consists of three stages, and hence termed as “Tri-scan” enhancement: (1) tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics; (2) mucosa layer enhancement: an adaptive sigmoid function is employed on the R plane of the image to highlight micro-vessels of the superficial layers of the mucosa and submucosa; and (3) color tone enhancement: the pixels are uniformly distributed to create an enhanced color effect to highlight the subtle micro-vessels, mucosa and tissue characteristics. The proposed method is used on a large data set of low contrast color white light images (WLI). The results are compared with three existing enhancement techniques: Narrow Band Imaging (NBI), Fuji Intelligent Color Enhancement (FICE) and i-scan Technology. The focus value and color enhancement factor show that the enhancement level achieved in the processed images is higher compared to NBI, FICE and i-scan images.     

远程寄生虫卵图像计量分析工具的开发

为了充分实现远程寄生虫卵显微镜图像分析的技术,开发了一套用于远程寄生虫卵图像计量分析的软件工具。在原有的虫卵自动分割技术上加入了自动测量寄生虫卵显微图像大小、形状和卵壳厚度的计量分析功能,并嵌入到“病原体远程监测网站”上。为医生以及研究工作者提供了辅助定量分析工具,使得鉴别显微镜样本图片中寄生虫卵的工作变得简单高效。同时本文采用一种无刷新的网站实现方案,在保证用户良好体验的前提下,满足系统初期保存用户上传样本图片的需求。     

利用E-film软件实现放射影像的DVD存档

本文介绍了我院放射科利用E-film软件实现放射影像资料存档的方法.阐述了该方法具有成本低廉、简单易行等特点.     

瓦片式算法在家庭网络环境实现高分辨率医学影像实时浏览与传输的研究

以DICOM影像为主的高分辨率医学影像数据共享在医学信息化领域有着重要意义。由于网络带宽、通信成本等原因,使得医学影像资源共享受到较大限制。借鉴GIS瓦片式切陶算法对地理影像数据分割存储、按需传输的思想,移植到医学影像共享服务,通过自主推导的公式模型,在保证医学影像质量无损伤的前提下,大幅度压缩远程浏览时的影像传输信息量,为医学影像的低成本远程交流提供一种新的思路。并以此为基础设计了一套Web远程医学影像浏览平台,确保用广,无需安装仟何软件／插件,以普通家庭刚络连接方式流畅浏览高分辨率影像数据。经过验证,效果理想。     

浅谈介入放射学的教学

在介入放射学教学中,利用多种教学手段使学生在短时间内全面了解介入放射学,不断提高临床思维和影像思维能力,为培养创新型介入放射学专业人才奠定基础。     

2种铺无菌盘方法比较

无菌盘是将无菌巾铺在清洁干燥的治疗盘内,形成一无菌区,放置无菌物品,以供治疗、护理用,有效时间不超过4h。临床上常用的铺无菌盘法为单层底铺巾法,方法如下：打开无菌包,取1块治疗巾放于治疗盘内,捏住无菌巾一边外面两角,抖开,双折铺于治疗盘上,上层向远端扇形折叠,开口向外,放入无菌物品后,拉平上层盖于物品上,上下边缘对齐,向上翻折开口处2次,两侧边缘向下翻折1次,以保持无菌。     

A Decision Support Tool for Health Service Re-design

Many of the outpatient services are currently only available in hospitals, however there are plans to provide some of these services alongside with General Practitioners. Consequently, General Practitioners could soon be based at polyclinics. These changes have caused a number of concerns to Hounslow Primary Care Trust (PCT). For example, which of the outpatient services are to be shifted from the hospital to the polyclinic? What are the current and expected future demands for these services? To tackle some of these concerns, the first phase of this project explores the set of specialties that are frequently visited in a sequence (using sequential association rules). The second phase develops an Excel based spreadsheet tool to compute the current and expected future demands for the selected specialties. From the sequential association rule algorithm, endocrinology and ophthalmology were found to be highly associated (i.e. frequently visited in a sequence), which means that these two specialties could easily be shifted from the hospital environment to the polyclinic. We illustrated the Excel based spreadsheet tool for endocrinology and ophthalmology, however, the model is generic enough to cope with other specialties, provided that the data are available.     

A Visual Decision Support Tool for Appendectomy Care

Appendectomy is the most common abdominal surgical procedure performed in children in the United States. In order to assist care providers in creating treatment plans for the postoperative management of pediatric appendicitis, we have developed a predictive statistical model of outcomes on which we have built a prototype decision aid application. The model, trained on 3724 anonymized care records and evaluated on a separate set of 2205 cases from a tertiary care center, achieves 97.0% specificity, 25.1% true sensitivity, and 58.8% precision. We have also built an interactive decision support tool augmented with simple visualization techniques designed for clinicians to use in the course of making care decisions (e.g., discharge) and in patient/stakeholder communication. Its focus is on end-user ease of use and integration into existing clinician workflows, and is designed to evolve its predictions as more and better data become available.