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About Me (Email:

I have been working on biomedical image analysis since I started my PhD study in 2003, and I have developed more than 20 computational tools such as image segmentation and registration for brain magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), microscopy images, breast dynamic contrast enhanced MRI (DCE-MRI), and lung 4D computed tomography (CT) images.

Currently, I am currently a faculty member of Psychiatry Department and BRIC in the University of North Carolina (UNC) at Chapel Hill . My research is focused on fast and robust analysis of large population data, computer assisitted diagnosis, and computational medicine.

Research Thrusts

Computational Anatomy

Computational Anatomy (CA) is emerging as a discipline focused on the quantitative analysis of variability of biological shape. Applications of CA in brain science have developed rapidly, with applications of brain mapping technologies that provide mechanisms for discovering neuropsychiatric disorders of many types. My research focuses on quantiative measurement tools and cross-section/longitudinal studies on brain development and brain diseases such as Alzheimer's Disease, and Autism.

Computer Asisted Diagnosis

An accurate and early diagnosis of brain disorders is of fundermental importance for the development of effective treatment to palliate the effects of the diseases. Computer-aided diagnosis (CAD) is very useful in improving the prediction accuracy and complementing the neuropsychological assessments performed by expert clinicians. One of my onging project is in length of longitudinal analysis for early diagnosis of Alzheimer’s disease.

Image Guided Radiation Therapy

Image-guided radiation therapy (IGRT) is the most advanced technology to track cancer and spare normal tissues. IGRT is the type of radiation a cancer patient wants because this technology decreases the radiation dose to normal tissue, thus decreasing side effects and improving outcomes. Our team is pursuiting for improving the radiation therapy for lung cancer by advanced image processing and machine learning techniques.