Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints

Jiang, Xiaoliang; Wang, Qiang; He, Bin; Chen, Shaojie; Li, Bailin

doi:10.1016/j.neucom.2016.03.046

Cited by 56 publications

(24 citation statements)

References 26 publications

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“…In addition to using visual evaluation, the accuracy of the target region segmentation can be assessed quantitatively and objectively using the DICE coefficient (DICE) [51,52] and the Jaccard similarity index (JSI) [53]. Following the experimental techniques designed in [42,54], test images are selected randomly from the BSDS500 database.…”

Section: Comparative Evaluation Resultsmentioning

confidence: 99%

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Sun

Meng

et al. 2018

Applied Sciences

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Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF-and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.2 of 20 an object with strong edges; however, they cannot detect the weak edges of an object. Moreover, the methods are sensitive to noise and do not easily obtain satisfactory segmentation results for blurred images [2]. In addition, the contour should initially be set near the object; otherwise, it is difficult to obtain correct segmentation results [17]. Region-based models make full use of image statistical information, whereas edge-based models do not. Thus, region-based models have multiple advantages over edge-based models. For example, because regional information is used, region-based models are less sensitive to contour initialization and noise. Furthermore, these region-based models can easily segment images with weak boundaries or even those without boundaries [18]. One of the most typical region-based methods was proposed by Chan and Vese (C-V) [11], which is based on the Mumford-Shah functional [19]. The C-V model is based on the assumption that image intensities are homogeneous in each region. However, this assumption does not suit the intensity of inhomogeneous images, which limits the method's further applications [20,21].Recently, hybrid methods have gained popularity among region-based methods. These methods combine region (local or global) and edge information in their energy formulations [22]. Zhang et al. [23] proposed the selective binary and Gaussian filtering regularized level set (SBGFRLS) model. This model combines the advantages of region-based and edge-based active contours and introduces a region-based SPF function, which utilizes the image global intensity means from the C-V method. This metho...

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Section: Comparative Evaluation Resultsmentioning

confidence: 99%

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Sun

Meng

et al. 2018

Applied Sciences

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“…The central idea is to represent the evolving contour using a signed function whose zero corresponds to the actual contour. Then, according to the motion equation of the 221 contour, one can easily derive a similar flow for the implicit surface that when applied to the zero level will reflect the propagation of the contour [12]. The level set method affords numerous advantages: it is implicit, is parameter-free, provides a direct way to estimate the geometric properties of the evolving structure, allows for change of topology, and is intrinsic.…”

Section: Materials and Methodologymentioning

confidence: 99%

Brain Image Segmentation Based on Fuzzy Clustering

Mohammed

2018

MJS

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The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

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“…It involves the summation of membership functions in the neighborhood of the individual pixel under consideration [29]. This makes it a robust tool for noisy image segmentation.…”

Section: Fcm Algorithmmentioning

confidence: 99%

“…Furthermore, with p and q being parameters to control the relative importance of membership, the spatial function is incorporated into the FCM membership function as [32]: …”

Section: Fcm Algorithmmentioning

confidence: 99%

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

Oluwasanmi¹,

Qin²,

Lan³

2018

dtcse

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This study presents an automatic segmentation of the brain tissues in Magnetic Resonance Image using a fusion of Spatial Fuzzy C-Means (sFCM) and K-Means Algorithms (sFCMKA). The segmentation of the standard FCM algorithm have been realized to be highly sensitive to noise and therefore fail in providing accurate results. The K-Means algorithm while having the advantage of simplified clustering results is disadvantaged for merely classifying pixels into just one cluster segment. In overcoming this problem, first, FCM algorithm incorporated with a spatial neighborhood function is presented to eliminate the effect of noise. Also, the K-Means algorithm is embedded to sort pixels in tissues which are not intertwined with another. Since they are nonoverlapping tissues, such pixels are grouped in a singular cluster without having membership to another cluster segment. sFCMKA achieves this by placing a threshold parameter on pixels having contrasting cluster result from both algorithms and then reprocessed. The efficiency of the proposed algorithm is demonstrated by evaluating the results with the input image's manual segmentation using the Jaccard similarity index, Dice coefficient and accuracy index. The results of the proposed algorithm depicts a far more admirable accuracy quality compared to existing clustering based segmentation methods.

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Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints

Cited by 56 publications

References 26 publications

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Brain Image Segmentation Based on Fuzzy Clustering

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

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