PDE-based Image Restoration and Segmentation and Their Applications to Medical Imagery

基于偏微分方程的图像恢复和分割及其在医学图像中的应用

• 批准号: 0609815
• 负责人: Seongjai Kim
• 金额: --
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0609815&HistoricalAwards=false
• 关键词: PDE; based; Image; Restoration; Segmentation

The investigator and his colleagues develop novel diffusion-like PDEmodels and computational methods for image restoration and segmentationof medical imagery acquired from ultrasound, magnetic resonance (MR),computed tomography (CT), positron emission tomography (PET), and singlephoton emission computed tomography (SPECT) scanners.For restoration, the project investigates various PDE models and relatednumerical procedures that can effectively preserve and restore importantimage features, not only fine structures but also slow transitions,for various medical images in 2D and 3D.Given basic models derived from variational approaches, non-variationalvariants will be developed in order to optimize their performancesin image restoration, by integrating noise characteristics and byincorporating appropriate diffusion modulators and dynamic constraintterms.Conventional level set formulations of the Mumford-Shah functional insegmentation work well for essentially binary images; however, they mayfail to detect desired edges for general images, due to ambiguity in thecomputation of the complementary function (the piecewise cartoon image)and the ability to detect smooth boundaries.In order to overcome the difficulty, the project will develop variousmathematical and numerical techniques.The innovative models and computational algorithms will broadly impactvarious other fields, while enhanced knowledge on medical images willinstitute advancements on medical scanner design.The project develops state-of-the-art algorithms in image restorationand segmentation for medical imagery in both planar and volumetric formats.Although there have been remarkable advancements in medical scanner design,medical images can easily incorporate certain noise and various artifacts.It is extremely important to suppress such artifacts for an accuratemedical diagnosis.On the other hand, in various modern medical diagnoses and operations,computer algorithms are being utilized to detect-and-measure body partsautomatically; however, these algorithms are yet to be improved for moreaccurate feature detection.The investigator and his colleagues study various mathematical andcomputational algorithms in order to enhance the image quality andsegment important image features effectively.Besides, the project will advance imaging techniques for the reductionof radiation exposure to the patient at X-ray computed tomography (CT).Here the goal is to keep patient radiation exposures from CT as low aspossible while achieving the required image quality and medical benefit.The planned research will have an important impact on improvedunderstanding of the current mathematical image processing techniques,advance knowledge on medical images, and institute advancements onmedical scanner design.The research project will support a graduate student and accelerateactivities in a research group at Mississippi State University, calledthe IMage Processing And Computational Techniques (IMPACT) which isorganized by the investigator.All developed software will be freely shared with the community.

研究人员和他的同事开发了新的扩散样PDE模型和计算方法，用于从超声、磁共振（MR）、计算机断层扫描（CT）、正电子发射断层扫描（PET）和单光子发射计算机断层扫描（SPECT）扫描仪获取的医学图像的图像恢复和分割。该项目研究了各种PDE模型和相关的数值程序，这些模型和程序可以有效地保留和恢复重要的年龄特征，不仅是精细结构，而且还有缓慢的转变，针对二维和三维医学图像，给出了基于变分方法的基本模型，通过引入噪声特性、扩散调制器和动态约束项，发展了非变分方法，以优化其在图像恢复中的性能。然而，由于互补函数的计算模糊，它们可能无法检测到一般图像的期望边缘（分段卡通图像）和检测光滑边界的能力。为了克服这一困难，该项目将开发各种数学和数值技术。创新的模型和计算算法将广泛影响其他各个领域，同时，对医学图像的深入了解将促进医学扫描仪设计的进步。该项目为平面和体积格式的医学图像开发了最先进的图像简化和分割算法。尽管医学扫描仪设计取得了显著进步，医学图像很容易包含一定的噪声和各种伪影，抑制这些伪影对于准确的医学诊断是非常重要的。另一方面，在各种现代医学诊断和手术中，计算机算法被用于自动检测和测量身体部位;然而，这些算法还有待改进，以更准确地检测特征。研究者和他的同事们研究了各种数学和计算算法，以提高图像质量和有效地分割重要的图像特征。此外，该项目将推进成像技术，以减少X射线计算机断层扫描（CT）对患者的辐射暴露。在这里，我们的目标是尽可能降低CT对患者的辐射暴露，同时达到所需的图像质量和医疗效益。计划中的研究将对提高认识产生重要影响。目前的数学图像处理技术，先进的医学图像知识，并研究院先进的医疗扫描仪设计。该研究项目将支持研究生和加速活动，在密西西比州立大学的一个研究小组，所谓的图像处理和计算技术（IMPACT），这是由调查员组织。所有开发的软件将免费与社会共享。