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PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global...
Positron emission tomography - computed tomography (PETCT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a content-based image retrieval system for retrieving similar cases from an imaging database as a reference dataset to aid the physicians in PET-CT scan interpretation...
Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. In this paper, we propose the use of a variational segmentation...
Modeling of respiratory motion is very important for the efficacy of radiation therapy (RT) which is used in the treatment of cancer in the thorax and the abdomen. Having such a model is a key point to deliver, under breathing induced motion, less dose to the normal healthy tissues and higher dose to the tumor. Many methods have been developed to reduce the respiratory motion induced errors. While...
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