Since their introduction as a means of front propagation and their first application to edge-
based segmentation in the early 90's, level set methods have become increasingly popular …

Medical image segmentation is a fundamental and challenging problem for analyzing
medical images. Among different existing medical image segmentation methods, graph …

Automatic segmentation of subcortical structures in human brain MR images is an important
but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity …

We introduce a new methodology that combines deep learning and level set for the
automated segmentation of the left ventricle of the heart from cardiac cine magnetic …

Combinatorial graph cut algorithms have been successfully applied to a wide range of
problems in vision and graphics. This paper focusses on possibly the simplest application of …

Image segmentation is a fundamental problem in computer vision. Despite many years of
research, general purpose image segmentation is still a very challenging task because …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional
image and video data. The segmentation problem is cast as approximate energy …

In recent years, segmentation with graph cuts is increasingly used for a variety of
applications, such as photo/video editing, medical image processing, etc. One of the most …

We present a new supervised learning model designed for the automatic segmentation of
the left ventricle (LV) of the heart in ultrasound images. We address the following problems …