Abstract
This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF .
Highlights
Image segmentation is an elementary task in the field of image processing and widely applied in image analysis, computer vision, medical imaging, etc. [1]
By introducing the weighted global region-based SPF function and the weighted local region-based SPF function, we proposed a novel active contour model driven by weighted hybrid region-based signed pressure force to effectively segment inhomoge-neious and noisy images
The codes of the C-V and Online Regionbased Active Contour Model (ORACM) models can be downloaded from the url: https://ww2.mathworks.cn/matlabcentral/fileexchange/ 49034-oracm-online-region-ased-active-contour-model
Summary
Image segmentation is an elementary task in the field of image processing and widely applied in image analysis, computer vision, medical imaging, etc. [1]. The M-S model utilizes the global intensity difference between the average intensities of the inner and outer regions of the evolving curve to guide the contour moving toward the object boundaries It can achieve the desired segmentation for homogeneous images. The energy function is formulated via maximum aposteriori (MAP) estimation of contours, minimizing the energy function is carried out using the Euler-Lagrange equations of local region statistics Followed by this idea, Zhang et al [13] proposed a local image fitting (LIF) model based on Gaussian filtering function with a local fitted image. In light of above analysis, we propose a new active contour driven by Weighted Hybrid Region-based SPF to segment images in the present of intensity InH and noise, called WHSPF. The optimal constants c1 and c2 can be far different from the original image with intensity InH
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