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The increasing numbers of patient scans and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a need for efficient PET volume handling and the development of new volume analysis approaches to aid clinicians in the diagnosis of disease and planning of treatment. A novel automated system for oncological PET volume segmentation is proposed in this paper...
A medical image retrieval system combined of the low-level image feature and high-level semantic is used in the paper witch includes two main parts: image preprocessing and the machine learning. In the first part, feature tree structure is presented to reduce the semantic gap and in the latter part, a novel machine learning method based on SVM is presented to optimize the Network parameters by which...
Image segmentation still requires improvements although there have been research work since the last few decades. This is due to some factors. Firstly, most image segmentation solution is problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. The goal of this work is to design...
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