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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636990
• 关键词: Novel; Image; Texture; Analysis; Advanced

Novel Image Texture Analysis for Advanced Structure CharacterizationMy 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型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型信号处理和分析算法。目前，我正在开发新的技术，以改进生物受试者的磁共振成像(MRI)对组织属性的评估。然而，我的方法在包括地球科学、物理和工程在内的不同领域有更广泛的应用。傅立叶变换构成了包括磁共振成像和计算机断层成像(CT)在内的许多成像方式的基石。MR数据最初在频域中采集，然后使用逆傅立叶变换进行重建。因此，可以使用先进的、基于傅立叶分析的方法来处理磁共振图像，以增强对组织结构的识别而不丢失信息。在过去的几年里，我一直致力于开发和评估先进的方法，以检测图像强度局部模式的细微变化，也就是MR“纹理”。纹理图案通常太过微妙，即使是训练有素的观察者也无法察觉，因此需要数学算法。我的实验数据证明纹理分析是组织损伤和修复的敏感措施。在这项提议中，我的目标是开发新的纹理分析方法，以提高加拿大人的健康结果。特别是，我建议对纹理光谱成像和纹理各向异性进行研究，以提高纹理分析的敏感性和特异性。这个项目的结果可能会提高我们在不修改现有成像方案的情况下评估组织结构的能力。