Open AccessComparison of level set models in image segmentation
Author(s) -
Rahmat Roushanak,
HarrisBirtill David
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5796
Subject(s) - image segmentation , artificial intelligence , computer science , computer vision , segmentation , scale space segmentation , image (mathematics) , set (abstract data type) , level set (data structures) , segmentation based object categorization , pattern recognition (psychology) , programming language
Image segmentation is one of the most important tasks in modern imaging applications, which leads to shape reconstruction, volume estimation, object detection and classification. One of the most popular active segmentation models is level set models which are used extensively as an important category of modern image segmentation technique with many different available models to tackle different image applications. Level sets are designed to overcome the topology problems during the evolution of curves in their process of segmentation while the previous algorithms cannot deal with this problem effectively. As a result, there is often considerable investigation into the performance of several level set models for a given segmentation problem. It would therefore be helpful to know the characteristics of a range of level set models before applying to a given segmentation problem. In this study, the authors review a range of level set models and their application to image segmentation work and explain in detail their properties for practical use.