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Lung segmentation is an important first step towards an automated CAD (Computer Aided Detection) system for a variety of medical applications. These applications range from lung nodule detection for identifying cancerous tumors to acinar shadow detection for identifying Tuberculosis. In our prior work we had used the Concave Hull algorithm for lung segmentation. However, our results showed over segmentation...
Motion data is quickly expanding its application scope, following the recent advancements in smart sensing technology. In particular, it has been shown to be helpful for objective measurement and assessment of surgical dexterity among users at different levels of training. The goal is to allow trainees to evaluate their performance based on a reference set of hand movements. Similar to other multimedia...
Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM)...
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