摘要翻译：
本文主要研究极值层的极值区域(EREL)选择问题。EREL是最近提出的一种特征检测器，旨在从一组极值区域中检测区域。这是血管内超声(IVUS)图像中动脉壁边界分割的一个分支问题。对于每个IVUS帧，生成一组EREL区域来描述人冠脉腔区。每个EREL然后由一个椭圆拟合，以表示腔体边界。目标是分配最合适的EREL作为管腔。在这项工作中，EREL选拔分两轮进行。在第一轮中，分析一组EREL区域中的模式，并使用该模式生成近似的luminal区域。然后，计算该近似区域与每个EREL之间的二维相关系数，以保持相关性最强的区域。在第二轮中，为每个EREL及其拟合椭圆计算紧致度量，以保证所得到的EREL不受常见伪影（如分叉、阴影和侧枝）的影响。我们根据Hausdorff距离(HD)和Jaccard测度(JM)在一个公开的数据集上的训练和测试集对所选择的ERELs进行了评估。结果表明，我们的选择策略优于当前最先进的选择策略。
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英文标题：
《EREL Selection using Morphological Relation》
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作者：
Yuying Li and Mehdi Faraji
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  This work concentrates on Extremal Regions of Extremum Level (EREL) selection. EREL is a recently proposed feature detector aiming at detecting regions from a set of extremal regions. This is a branching problem derived from segmentation of arterial wall boundaries from Intravascular Ultrasound (IVUS) images. For each IVUS frame, a set of EREL regions is generated to describe the luminal area of human coronary. Each EREL is then fitted by an ellipse to represent the luminal border. The goal is to assign the most appropriate EREL as the lumen. In this work, EREL selection carries out in two rounds. In the first round, the pattern in a set of EREL regions is analyzed and used to generate an approximate luminal region. Then, the two-dimensional (2D) correlation coefficients are computed between this approximate region and each EREL to keep the ones with tightest relevance. In the second round, a compactness measure is calculated for each EREL and its fitted ellipse to guarantee that the resulting EREL has not affected by the common artifacts such as bifurcations, shadows, and side branches. We evaluated the selected ERELs in terms of Hausdorff Distance (HD) and Jaccard Measure (JM) on the train and test set of a publicly available dataset. The results show that our selection strategy outperforms the current state-of-the-art.
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PDF链接：
https://arxiv.org/pdf/1806.0358