A robust hybrid active contour model based on pre-fitting bias field correction for fast image segmentation

An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper Th...

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Veröffentlicht in:Signal processing. Image communication Jg. 97; S. 116351
Hauptverfasser: Lei, Yu, Weng, Guirong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.09.2021
Elsevier BV
Schlagworte:
Active contour model
Adaptive edge indicator function
Algorithms
Bias
Bias field
Contours
Image segmentation
Intensity inhomogeneity
Noise sensitivity
Robustness
Scientific papers
Active contour model
Adaptive edge indicator function
Image segmentation
Intensity inhomogeneity
Bias field
ISSN:0923-5965, 1879-2677
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Zusammenfassung:An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper The energy functional is formulated through incorporating the adaptive edge indicator function and level set formulation driven by bias field correction. The adaptive edge indicator function, which is formulated based on image gradient information, is utilized to detect object boundaries and accelerate the segmentation in the homogeneous region. The level set formulation is constructed based on an improved criterion function, in which bias field information is considered. Specifically, the bias field distribution is approximated through the local mean gray value algorithm as a prior. Moreover, a new regularized function is proposed so as to maintain the stability of curve evolution. The segmentation process is implemented by the optimized energy function and the novel regularized term. Compared to previous active contour models, the modified active contour method can yield more precise, stable, and efficient segmentation results on some challenging images. •A hybrid active contour model based on bias correction is proposed.•The bias field is calculated through a new method before curve iteration.•An adaptive edge indicator function and a new regularization function is given.•Experiments show the superiority of the proposed model such as high efficiency, robustness and accuracy.
Bibliographie:ObjectType-Article-1
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ISSN:0923-5965
1879-2677
DOI:10.1016/j.image.2021.116351