Joint Metal Artifact Reduction And Segmentation Of Ct Images Using Dictionary-Based Image Prior And Continuous-Relaxed Potts Model

2015 IEEE International Conference on Image Processing (ICIP)(2015)

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Segmenting interesting objects from CT images has a wide range of applications. However, to achieve good results, it is often necessary to apply metal artifact reduction to raw CT images before segmentation. While there has been a great deal of research focusing on metal artifact reduction and segmentation as individual tasks, there have been very few attempts to solve the two problems jointly. We present a novel approach to solve the problem of segmenting raw CT images with metal artifacts, without the access to the raw CT data. Given an approximate metal artifact mask, the problem is formulated as a joint optimization over the restored image and the segmentation label, and the cost function includes a dictionary-based image prior to regularize the restored image and a continuous-relaxed Potts model for multi-class segmentation. An effective alternating method is used to solve the resulting optimization problem. The algorithm is applied to both simulated and real datasets and results show that it is effective in reducing metal artifacts and generating better segmentations simultaneously.
Metal Artifact Reduction,Potts Model Segmentation,Dictionary Learning,Security CT
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