Open AccessFast localised active contour for inhomogeneous image segmentation
Author(s) -
Duc Bui Toan,
Ahn Chunsoo,
Shin Jitae
Publication year - 2016
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.2015.0489
Subject(s) - active contour model , computer science , segmentation , image segmentation , artificial intelligence , scale space segmentation , image (mathematics) , computational complexity theory , computer vision , function (biology) , key (lock) , segmentation based object categorization , homogeneous , pattern recognition (psychology) , algorithm , mathematics , computer security , combinatorics , evolutionary biology , biology
The localised active contour framework has been widely used for image segmentation because it provides reliable results for inhomogeneous images. However, its computational complexity remains an issue. In this study, the authors introduce a fast algorithm based on the localised active contour framework. A key concept of the proposed algorithm is its consideration of the curve evolution based on the speed function only at active points that change across time, rather than at all points in a narrow band. This approach reduces computational time in the localised active contour. The authors additionally propose a modified speed function to address inhomogeneous image segmentation. The experimental results demonstrate significant advantages of the proposed method over existing methods, both in terms of computational efficiency and segmentation accuracy, for homogeneous and inhomogeneous images.