Generalized scale: Theory, algorithms, and applications in image analysis
Generalized scale: Theory, algorithms, and applications in image analysis
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广义尺度：图像分析的理论、算法和应用
DOI：
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
A. Madabhushi
中科院分区：
文献类型：
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作者：
A. Madabhushi
广义尺度：理论、算法和在图像分析中的应用Anant Madabhushi主管：Jayaram K. Udupa磁共振（MR）成像（MRI）是一种非侵入性的人体成像方法，并彻底改变了医学成像。MR图像处理，特别是分割和分析，广泛用于医学和临床研究，以促进我们对人体各种疾病的理解，用于诊断，并用于制定治疗策略。这些努力面临两个主要困难，第一个是由于作为背景变化分量存在的图像强度不均匀性，第二个是由于MR图像强度灰度的非标准性。尺度是几乎所有图像处理和分析任务中有用的基本概念。一般来说，尺度相关的工作可以分为多尺度表示（全局模型）和局部尺度模型。在这篇论文中，我们提出了一个新的形态尺度模型，我们称之为广义尺度，它结合了局部尺度模型的属性和多尺度表示的全局精神。我们认为，这种半局部自适应性质的广义尺度赋予它某些明显的优势，比其他尺度配方，使其易于适用于解决一系列一般的图像处理任务。然而，在本论文的上下文中，我们仅限于解决与MR图像处理相关的两个问题，即1）校正图像强度不均匀性（称为不均匀性校正），以及2）校正MR图像强度标度的非标准性（称为强度标准化）。虽然在MR图像分析中强度标准化和不均匀性校正的重要性已经得到了很好的确立，但是它们的行为仅被孤立地研究，并且它们彼此之间的影响迄今尚未得到解决。本论文的部分工作集中在研究一种方法对另一种方法可能产生的影响，以找到产生最佳整体图像质量的不均匀性校正和强度标准化操作序列。我们的方法对近6000个3D临床和幻影MR数据集进行了广泛的定量和定性评价。
GENERALIZED SCALE: THEORY, ALGORITHMS, AND APPLICATIONS IN IMAGE ANALYSIS Anant Madabhushi Supervisor: Jayaram K. Udupa Magnetic Resonance (MR) Imaging (MRI) is a non-invasive method for imaging the human body and has revolutionized medical imaging. MR image processing, particularly segmentation, and analysis are used extensively in medical and clinical research for advancing our understanding of the various diseases of the human body, for their diagnosis, and for developing strategies to treat them. These efforts face two major difficulties the first due to image intensity inhomogeneity present as a background variation component, and the second due to the non-standardness of the MR image intensity gray scale. Scale is a fundamental concept useful in almost all image processing and analysis tasks. Broadly speaking, scale related work can be divided into multi-scale representations (global models) and local scale models. In this thesis, we present a new morphometric scale model that we refer to as generalized scale which combines the properties of local scale models with the global spirit of multi-scale representations. We contend that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other scale formulations, making it readily applicable to solving a range of general image processing tasks. In the context of this thesis however, we limit ourselves to addressing two issues that are relevant to MR image processing, namely, 1) correcting for image intensity inhomogeneity (referred to as inhomogeneity correction), and 2) correcting for the non-standardness of the MR image intensity scale (referred to as intensity standardization). While the importance of intensity standardization and inhomogeneity correction in MR image analysis is well established, their behavior has been studied only in isolation, and their influence on each other has thus far not been addressed. Part of this thesis work is centered on studying the possible effects of one method on the other, in order to find the sequence of inhomogeneity correction and intensity standardization operations that will produce the best overall image quality. The results of extensive quantitative and qualitative evaluation of our methods on nearly 6000 3D clinical and phantom MR data sets are also presented.
DOI：
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发表时间：
1994
期刊：
AJNR. American journal of neuroradiology
影响因子：
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作者：
Lexa,FJ;Grossman,RI;Rosenquist,AC
通讯作者：
Rosenquist,AC
影响因子：
19.7
作者：
DOUSSET, V;GROSSMAN, RI;COHEN, JA
通讯作者：
COHEN, JA