An Active Contour Model Based on Local Pre-piecewise Fitting Bias Corrections for Fast and Accurate Segmentation

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Wang, Guina, Zhang, Fuzheng, Chen, Yiyang, Weng, Guirong, Chen, Hongtian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: active contour model; Bias; bias correction; Computational modeling; Contours; Deep learning; Fitting; Image segmentation; Level set; level set method; Load modeling; local pre-piecewise fitting; Object segmentation; Optimization; Regularization; Robustness
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3241981

The lack of grasp of the image information and the unstable fluctuation of the model energy may cause segmentation failure of the active contour model (ACM). Minimizing the impact of these two factors is critical. A local pre-piecewise fitting (LPPF) bias correction model for fast and accurate segmentation is proposed in this paper. It defines a pre-fitting function of local regions and an energy function. The grayscale information of small areas in the image is fully extracted, so that the contour accurately locates the target. Then, the optimal solution to the estimated value of the bias field is obtained. The real image information is described by the bias field, and the energy function of the model is constructed. The optimized distance regularized term and neighborhood average filtering method are utilized to achieve level set function regularization and contour smoothing. This optimization process reduces the amount of calculation and improves the robustness of LPPF model. Experiments are performed to verify that LPPF model has strong robustness to initial contours and has ability to segment blurry images while satisfactory segmentation efficiency and accuracy are obtained.