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感兴趣区域高效提取算法(英文)  BibTex
作者： 张红梅 卞正中 郭佑民 叶敏
单位： 西安交通大学生命科学与技术学院 陕西西安710049 (张红梅;卞正中);西安交通大学第一附属医院影像中心 陕西西安710061 (郭佑民);西安交通大学机械学院 陕西西安710049(叶敏)
关键词： 感兴趣区域;曲线进化;多尺度策略;多初始化快速推进算法;分割
出处： 软件学报 2005 年 01期
基金： 国家自然科学基金;卫生部临床学科重点项目~~
全文链接： 查看全文>>
摘要：
　　感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进Snake模型.ROI信息能够增强曲线深入到对比度低且细窄的区域中的传播能力.其次,提出了多初始化快速推进算法,选择性地种植种子曲线有助于局部区域的生长从而进一步改善分割结果.此外,为提高计算效率,在多尺度空间进行数值求解,其中利用快速解传递方法实现粗一级尺度到细一级尺度解的传递,可以加速收敛.利用医学图像分割实验对该方法进行评估,结果表明:该方法能够快速、精确地提取低对比度和细窄的ROI区域.与现有方法相比,该方法的高效性同时体现在分割结果和计算代价上.
正文快照：
　　1 Introduction Region of interest (ROI) plays a crucial role in medical image analysis. Quantitative analysis of the shape and properties of ROI could provide reliable data for diagnosing disease and the follow-up treatment planning[1]. As a result, exploiting an accurate and fast ROI extraction method is in a great need. In recent years, ROI extraction based on the curve evolution approaches that deform an initial curve towards the desired boundary has been extensively explored. Snake…
An Efficient Approach to Extraction of Region of Interest
Author： ZHANG Hong-Mei1+;BIAN Zheng-Zhong1;GUO You-Min2;YE Min3 1(School of Life Science and Technology;Xian Jiaotong University;Xian 710049;China) 2(Imaging Center of the First Affiliated Hospital;Xian Jiaotong University;Xian 710061;China) 3(School of Mehanical Engineering;Xian Jiaotong University;Xian 710049;China)
Keywords： ROI(region of interest);curve evolution;multiscale scheme;multi-initial fast marching algorithm;segmentation
Abstract：
　ROI (region of interest) plays an important role in medical image analysis. In this paper, an efficient approach to ROI extraction based on monotonically marching curve evolution is proposed. The improvement is in two aspects: first, a new monotonically marching snake integrating ROI information is presented by minimizing the new defined ROI energy. Due to the region based speed term, the front could even propagate in low contrast and narrow thin areas. Second, a multi-initial fast marching algorithm is developed for numerical implementation, where a multi-initial scheme can perform the selective growth of the front, thus further reduce the front leaking. Furthermore, a multiscale scheme for numerical implementation is adopted, where a fast passing solution method is used for determining the initial solution on the finer scale that greatly reduces the computational cost. The validity of the proposed approach is demonstrated on the medical image ROI extraction. Experimental results show that the approach is efficient both in computational cost and segmentation quality. Low contrast and narrow thin ROI could be efficiently extracted precisely by the approach.