Assistant Professor of Computer Science, Faculty of Science
Dr. Xin Chen's research interests are in the areas of image processing, computer vision and machine learning, particularly applied to medical image analysis.
One primary area of Dr. Chen's research is motion analysis in radiology imaging. He developed techniques to construct statistical shape and motion models of bones in articulated joint from CT images. This generic population-based models were then employed to estimate individual patient positioning in radiotherapy and to detect abnormalities in wrist pathology based on 2-D radiographs using 3-D to 2-D image registration techniques. Besides working on motion estimation, he also established researches in respiratory motion correction in MRI, which can be potentially applied to the latest state-of-the-art integrated MRI/PET acquisition.
Another area of Dr. Chen's research is to apply machine learning methods for medical image segmentation and disease biomarkers quantification. He and his formal colleagues developed an automatic nerve fibre segmentation and quantification tool which enabled early detection of peripheral neuropathy for diabetic patients using corneal confocal microscopic images. Moreover, a similar machine learning framework was developed for automatic segmentation of fat, glandular tissue, pectoral muscle and nipple in breast mammograms, which further leaded to the automatic estimation of 3-D volumetric breast density based on a single 2-D digital mammogram.
Dr. Chen is always open to new research areas and keen to explore exciting applications using computer vision and machine learning methods.
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