4 April 2018 Hybrid active contour model for inhomogeneous image segmentation with background estimation
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Abstract
This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.
© 2018 SPIE and IS&T
Kaiqiong Sun, Kaiqiong Sun, Yaqin Li, Yaqin Li, Shan Zeng, Shan Zeng, Jun Wang, Jun Wang, } "Hybrid active contour model for inhomogeneous image segmentation with background estimation," Journal of Electronic Imaging 27(2), 023018 (4 April 2018). https://doi.org/10.1117/1.JEI.27.2.023018 . Submission: Received: 27 September 2017; Accepted: 15 March 2018
Received: 27 September 2017; Accepted: 15 March 2018; Published: 4 April 2018
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