|Type||Master thesis presentation|
|Title||Automated Abdominal Tissue Segmentation of Multicontrast Magnetic Resonance Images|
|Abstract||Abdominal fat tissue and liver volume are interesting in studies of many
diseases, e.g. cardiovascular, diabetes, obesity. Although there have been only
few works which developed automated or semi-automated abdominal tissue
Magnetic Resonance Imaging (MRI) is a medical imaging technology that provides rich information about soft body tissues. Properties of MRI can give complementary contrast information from the body tissues.
Fuzzy c-means (FCM) clustering method assigns pixels of the image to different clusters according to their distance to the cluster centres in a feature space. But the original FCM does not utilize any spatial information for the segmentation, which is crucial in many cases, and especially in medical images.
In this master thesis we have acquired different MRI sequences and combined them in order to form an intensity feature space for an unsupervised spatial and original FCM classification.