A fast level set method for inhomogeneous image segmentation with adaptive scale parameter

Huang, Guopeng; Ji, Hongbing; Zhang, Wenbo

doi:10.1016/j.mri.2018.05.011

Cited by 28 publications

(22 citation statements)

References 23 publications

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“…The bias field is dependent on the initial position of the contour. Therefore, to make it independent of the initial position, the new bias field initialization is used from [41] as:…”

Section: Proposed Methodologymentioning

confidence: 99%

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Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

et al. 2020

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Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy. INDEX TERMS Active contours, bias field, image segmentation, intensity inhomogeneity, level set.

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“…The bias field is dependent on the initial position of the contour. Therefore, to make it independent of the initial position, the new bias field initialization is used from [41] as:…”

Section: Proposed Methodologymentioning

confidence: 99%

“…This incorrect bias field estimation may result in inaccurate segmentation results. To overcome this shortcoming, Huang, Ji, and Zhang formulated a new bias field initialization and assumed that the bias field varies slowly within the image domain, [41]. The initialization of the new bias field is…”

Section: F New Bias Field (Nbf) Initializationmentioning

confidence: 99%

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

et al. 2020

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“…During the same period, Zhou et al [28] considered the relationship between global intensity and local intensity and then devised a level set model based on an adaptive weight function to improve the accuracy of inhomogeneous intensity images. Later, a fast level set method [29] was proposed to segment inhomogeneous image segmentation using adaptive scale parameter, which can improve the robustness to initial contour and correct bias field accurately.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction

Zhang

Song

2019

IEEE Access

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Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 ± 0.1477 in Dice coefficient and 0.1350 ± 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively. INDEX TERMS Bias field correction, image segmentation, intensity inhomogeneity, level set model, medical image.

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“…Generally, the scale parameter for the clustering kernel function should be selected appropriately according to the degree of intensity inhomogeneity [1]. Recently, some adaptive scale models [32–34] and multiscale methods [35–37] are proposed to segment images with intensity inhomogeneity. Cai et al propose an adaptive scale ACM (ASACM) [33] based on image entropy and semi‐naive Bayesian classifier.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Huang

Zhang

et al. 2019

IET image process

Self Cite

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The level set method based on bias correction can segment images with gentle intensity inhomogeneity effectively. However, most level set methods fail to segment severe inhomogeneous images due to the use of fixed scale clustering criterion. To deal with this problem, an adaptive multilayer level set method is proposed to segment images with severe intensity inhomogeneity. First, an improved global adaptive scale operator and a local adaptive scale operator are designed to adaptively adjust the scale of clustering kernel function according to the degree of intensity inhomogeneity. Then, an adaptive multilayer level set structure is constructed with the two designed scale operators. The number of layers and the scale of each layer in the multilayer structure are adaptively determined based on the degree of intensity inhomogeneity, which not only provides appropriate candidate scales in each pixel but also allows the model to detect global contrast information. With the dual minimisation method, image segmentation and bias correction can be achieved simultaneously. In addition, a hybrid bias field initialisation procedure is proposed to enhance the robustness of the proposed method. Experimental results demonstrate the effectiveness and robustness of the proposed method in segmenting images with intensity inhomogeneity.

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A fast level set method for inhomogeneous image segmentation with adaptive scale parameter

Cited by 28 publications

References 23 publications

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction

Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

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