2020
DOI: 10.1109/access.2020.2975854
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Image Segmentation Using an Active Contour Model Based on the Difference Between Local Intensity Averages and Actual Image Intensities

Abstract: The local intensity fitting active contour models can handle inhomogeneous images, but they suffer from the shortcomings of poor performance in segmenting images with severe intensity inhomogeneity and being sensitive to initializations. To overcome these problems, we put forward a robust active contour model by introducing two adjustment coefficient functions. The energy functional of the proposed model is presented by integrating the local fitting term and two adjustment coefficient functions. The local fitt… Show more

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Cited by 10 publications
(3 citation statements)
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“…where c 1 , c 2 are defined in (11) and m x (x), m 2 (x) are defined in (19). From the definition, we can see that spf G (I(x)) is a global measure which contains the global statistical information, and spf L (I(x)) is a local measure which contains the local statistical information.…”
Section: Global and Local Fspfmentioning
confidence: 99%
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“…where c 1 , c 2 are defined in (11) and m x (x), m 2 (x) are defined in (19). From the definition, we can see that spf G (I(x)) is a global measure which contains the global statistical information, and spf L (I(x)) is a local measure which contains the local statistical information.…”
Section: Global and Local Fspfmentioning
confidence: 99%
“…2: Initialize the pseudo LSF u to be a constant function or a step function defined in (9). 3: Compute c i and m i (i = 1, 2) according to (11) and (19). 4: Update u according to (33), and then normalize it by (35).…”
Section: E Algorithmmentioning
confidence: 99%
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