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.

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对于2D和3D中的各种医学图像，还将开发出非变化的基本模型，并恢复重要的特征，不仅是精细的结构，而且还可以缓慢过渡。对于本质上是二进制图像；但是，由于互补功能（分段卡通图像）的含糊不清，它们可能会检测到一般图像的所需边缘，并能够检测光滑边界的能力。在克服难度的情况下，该项目将开发各种有害的和数值的技术。医学扫描仪设计。该项目在图像恢复中开发了最先进的算法和用于平面和体积格式的医学图像的细分，尽管在医学扫描仪设计中取得了显着的进步，医疗图像可以轻松地融合某些噪声和各种文物。算法被用来检测并衡量身体部分。然而，这些算法尚待改进以进行通讯特征检测。研究人员及其同事研究各种数学和计算算法，以便有效地增强图像质量和段落的重要图像特征，该项目将在X射线范围内降低辐射的辐射（降低X射线）（ct）的辐射（CT）。在实现所需的图像质量和医疗益处的同时，可能会产生重要的影响。计划的研究将对改善当前数学图像处理技术的了解，对医学图像进行提前知识以及对媒体扫描仪设计的进步。研究员。所有开发的软件将与社区免费共享。