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

该项目的主要目标是能够从成像数据(如DTI磁共振脑图像)中识别和分割特定的感兴趣结构。这些区域可能很小且不明显，对比度较差，因此，直接应用经典的无监督分割(旨在提取显著区域)是有问题的。这里追求的替代方案是一种在图像分割过程中利用类似专家的高级建议的系统：为此，底层引擎被赋予编码(A)用户过去在分割相似图像时已经花费的努力以及(B)来自相似图像队列的聚集知识的全局约束。关键的算法部分是设计将这些约束(尽可能好地)转换为组合框架的机制，以便针对高分辨率的3D成像数据有效地优化所得到的模型。这项研究为这一重要的图像分析任务提供了方法论和配套软件。该项目具有广泛的科学影响。这项研究产生的代码的广泛分发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，本文提出的算法在目标识别、图像分类等问题上也有应用。该项目也非常适合让来自不同背景的本科生和研究生参与尖端的跨学科计算机视觉和图像处理研究。

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.