A comparison of level set models in image segmentation
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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 are 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 paper we review a range of level set models and their application to image segmentation work and explain in detail their properties for practical use.
Rahmat , R & Harris-Birtill , D 2018 , ' A comparison of level set models in image segmentation ' , IET Image Processing , vol. 12 , no. 12 , pp. 2212-2221 . https://doi.org/10.1049/iet-ipr.2018.5796
IET Image Processing
© The Institution of Engineering and Technology 2018. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/ 10.1049/iet-ipr.2018.5796
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