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

点击翻译按钮获取中文摘要

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.