A study on liver vessel segmentation

Vessel segmentation is a key process for visualization, diagnosis and quantification of different segments of the medical images obtained by Computed Tomography (CT), Computed Tomography Angiography (CTA), multi-phase CT, multi-detector CT, Magnetic Resonance (MR), Magnetic Resonance Angiography (MRA) and other medical imaging techniques devoted particularly to vessels. This thesis gives an overview on liver vessel segmentation methods applied to the images obtained by any medical imaging technique.Liver segmentation is a necessary step for liver transplantation and also for diagnosing liver tumors. This thesis focuses on the liver vessel segmentation methods which can ultimately be used for liver transplantation and for liver tumor diagnosis.The vessel segmentation is realized based on 1) pattern recognition, 2) image processing, 3) optimization, 4) graph analysis, and 5) partial differential equation models. The methods in the pattern recognition group can further be classified into the following sub-groups in terms of the features used: 1) intensity based methods, 2) textural based methods, and 3) geometric based methods. The classification of the methods in the pattern recognition group can also be done in terms of the classifiers used as: 1) knowledge based methods, 2) unsupervised (clustering) based methods, 3) machine learning methods including Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), 4) probabilistic methods, and 5) hybrid methods. On the other hand, image processing based segmentation methods can be categorized into subgroups based on topological, morphological and intensity-spatial information.