RI: Small: Endowing Graph-Based Image Segmentation with Global 'Advice': Applications to Diffusion Tensor Images

RI：小：为基于图的图像分割赋予全局“建议”：在扩散张量图像中的应用

• 批准号: 1116584
• 负责人: Vikas Singh
• 金额: $ 34.7万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116584&HistoricalAwards=false
• 关键词: RI; Small; Endowing; Graph; Based

The primary goal of this project is to enable identification and segmentation of specific structures of interest from imaging data (such as DTI MR brain images). These regions may be small and inconspicuous with poor contrast, therefore, direct application of classical unsupervised segmentation (designed to extract "salient" regions) is problematic. The alternative pursued here is a system to leverage expert-like high level advice within the image segmentation process: to do this, the underlying engine is endowed with global constraints encoding (a) effort already expended by the user in segmenting similar images in the past, as well as (b) aggregate knowledge from a cohort of similar images. The key algorithmic component is the design of mechanisms to translate such constraints (as best as possible) to a combinatorial framework so that the resultant models can be optimized efficiently for high resolution 3-D imaging data. This research produces the methodology and accompanying software for this important image analysis task. The project has broad scientific impact. Wide distribution of code produced from this research can enable improvements in various computer vision and medical imaging problems where image segmentation is a key step. Additionally, the algorithms developed here have applications in other problems such as object recognition and image categorization. The project is also well suited to involve undergraduate and graduate students from a diverse spectrum of backgrounds in cutting edge inter-disciplinary computer vision and image processing research.

该项目的主要目标是从成像数据（如DTI MR脑图像）中识别和分割特定的感兴趣结构。这些区域可能很小并且不显眼，对比度很差，因此，直接应用经典的无监督分割（旨在提取“突出”区域）是有问题的。这里所追求的替代方案是在图像分割过程中利用类似专家的高级建议的系统：为此，底层引擎被赋予全局约束，该全局约束编码（a）用户过去在分割类似图像时已经花费的努力，以及（B）来自一组类似图像的聚合知识。关键算法组件是设计将此类约束（尽可能好地）转化为组合框架的机制，以便可以针对高分辨率3D成像数据有效地优化生成的模型。这项研究产生的方法和配套软件，这一重要的图像分析任务。 该项目具有广泛的科学影响。从这项研究中产生的代码的广泛分布可以改善各种计算机视觉和医学成像问题，其中图像分割是关键步骤。此外，这里开发的算法在其他问题，如对象识别和图像分类的应用。该项目也非常适合来自不同背景的本科生和研究生参与尖端跨学科计算机视觉和图像处理研究。