An Image-Processing Strategy for the Segmentation and Quantification of Microaneurysms in Fluorescein Angiograms of the Ocular Fundus

Digital image-processing techniques can provide an objective and highly repeatable way of quantifying retinal pathology. This study describes an image-processing strategy which detects and quantifies microaneurysms present in digitized fluorescein angiograms. After preprocessing stages, a bilinear top-hat transformation and matched filtering are employed to provide an initial segmentation of the images. Thresholding this processed image results in a binary image containing candidate microaneurysms. A novel region-growing algorithm fully delineates each marked object and subsequent analysis of the size, shape, and energy characteristics of each candidate results in the final segmentation of microaneurysms. The technique is assessed by comparing the computer's results with microaneurysm counts carried out by five clinicians, using Receiver Operating Characteristic (ROC) curves. The performance of the automated technique matched that of the clinicians' analyses. This strategy is valuable in providing a way of accurately monitoring the progression of diabetic retinopathy.     

智慧主动型交通控制系统及实验

限于交通信息采集内容及方式、控制实现手段和优化模型性能等因素,传统交通控制系统难以满足实际的交通控制需求。大数据和车联网及车路协同系统等技术的发展,为提升交通控制系统的控制基础与条件、系统适应能力和响应能力创造了前所未有的条件。本文首先分析了信息、数据和计算技术等变革对交通系统运行环境、交通控制对象特征和通信与实现手段的影响;进而面向多模式交通流,提出了以更安全、更快速、更绿色、更人性化等为基本特征的下一代智慧主动型交通控制系统及其逻辑架构与物理架构。系统原型测试表明,快速发展的信息通信等技术及基于此的下一代交通控制系统具有出色的潜在性能和广阔的前景。     

Intelligent hybrid system for dark spot detection using SAR data

Synthetic Aperture Radars (SAR) are the main instrument used to support oil detection systems. In the microwave spectrum, oil slicks are identified as dark spots, regions with low backscatter at sea surface. Automatic and semi-automatic systems were developed to minimize processing time, the occurrence of false alarms and the subjectivity of human interpretation. This study presents an intelligent hybrid system, which integrates automatic and semi-automatic procedures to detect dark spots, in six steps: (I) SAR pre-processing; (II) Image segmentation; (III) Feature extraction and selection; (IV) Automatic clustering analysis; (V) Decision rules and, if needed; (VI) Semi-automatic processing. The results proved that the feature selection is essential to improve the detection capability, keeping only five pattern features to automate the clustering procedure. The semi-automatic method gave back more accurate geometries. The automatic approach erred more including regions, increasing the dark spots area, while the semi-automatic method erred more excluding regions. For well-defined and contrasted dark spots, the performance of the automatic and the semi-automatic methods is equivalent. However, the fully automatic method did not provide acceptable geometries in all cases. For these cases, the intelligent hybrid system was validated, integrating the semi-automatic approach, using compact and simple decision rules to request human intervention when needed. This approach allows for the combining of benefits from each approach, ensuring the quality of the classification when fully automatic procedures are not satisfactory.