Novel Image Texture Analysis for Advanced Structure Characterization

用于高级结构表征的新颖图像纹理分析

• 批准号: 418737-2012
• 负责人: Zhang, Yunyan
• 金额: $ 1.68万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573805
• 关键词: Novel; Image; Texture; Analysis; Advanced

Novel Image Texture Analysis for Advanced Structure Characterization My research seeks to develop, extend and validate novel signal processing and analysis algorithms for advanced structure characterization. Currently I am developing new techniques to improve the evaluation of tissue properties from magnetic resonance imaging (MRI) of biological subjects. However, my approaches have broader applications in diverse fields including geoscience, physics, and engineering. The Fourier transform forms the cornerstone of many imaging modalities including MRI and computer tomography (CT). The MR data are initially acquired in the frequency domain and are then reconstructed using the inverse Fourier transform. Therefore, MR images can be processed using advanced, Fourier-based analysis methods to enhance identification of tissue structure without loss of information. Over the past few years I have been engaged in developing and evaluation of advanced methods to detect subtle alterations in the local pattern of image intensity, namely, MR 'texture'. Texture patterns are generally too subtle to be perceived even by trained observers, thus mathematical algorithms are required. My pilot data demonstrate texture analysis to be a sensitive measure of tissue injury and repair. In this proposal, I aim to develop new avenues of texture analysis to enhance health outcomes of Canadians. In particular, I propose to investigate texture spectral imaging and texture anisotropy to improve the sensitivity and specificity of texture analysis. Output from this project may advance our capacity to evaluate tissue structure without modifying existing imaging protocols.

用于高级结构表征的新颖图像纹理分析 我的研究旨在开发、扩展和验证用于高级结构表征的新颖信号处理和分析算法。目前，我正在开发新技术，以改善生物受试者磁共振成像 (MRI) 组织特性的评估。然而，我的方法在地球科学、物理学和工程等不同领域有着更广泛的应用。 傅里叶变换构成了许多成像模式的基石，包括 MRI 和计算机断层扫描 (CT)。 MR 数据最初在频域中采集，然后使用傅里叶逆变换进行重建。因此，可以使用先进的基于傅里叶的分析方法来处理 MR 图像，以增强组织结构的识别，而不会丢失信息。 在过去的几年里，我一直致力于开发和评估先进方法，以检测图像强度局部模式（即 MR“纹理”）的细微变化。纹理图案通常太微妙，即使是训练有素的观察者也无法察觉，因此需要数学算法。我的试验数据表明纹理分析是组织损伤和修复的敏感指标。 在这项提案中，我的目标是开发纹理分析的新途径，以改善加拿大人的健康状况。特别是，我建议研究纹理光谱成像和纹理各向异性，以提高纹理分析的灵敏度和特异性。该项目的输出可能会提高我们在不修改现有成像协议的情况下评估组织结构的能力。