摘要：Automatic liver segmentation from abdominal images
is challenging on the aspects of segmentation accuracy, automation and
robustness. There exist many methods of liver segmentation and ways of categorisingthem. In
this paper, we present a new way of summarizing the latest achievements in
automatic liver segmentation. We categorise a segmentation method according to the
image feature it works on, therefore better summarising the performance of each
category and leading to finding an optimal solution for a particular
segmentation task. All the
methods of liver segmentation are categorized into three main classes including
gray level based method, structure based method and texture based method. In
each class, the latest advance is reviewed with summary comments on the
advantages and drawbacks of each discussed approach. Performance comparisons
among the classes are given along with the remarks on the problems existed and
possible solutions. In conclusion, we point out that liver segmentation is
still an open issue and the tendency is that multiple methods will be employed
together to achieve better segmentation performance.
