An active contour model based on local fitted images for image segmentation

Wang, Lei; Chang, Yan; Wang, Hui; Wu, Zhenzhou; Pu, Jiantao; Yang, Xiaodong

doi:10.1016/j.ins.2017.06.042

Cited by 113 publications

(42 citation statements)

References 44 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. Note that BSDS500 contains hundreds of natural images whose ground-truth segmentation maps have been generated by multiple individuals [40,55].…”

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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“…Active contour is a segmentation approach in which boundaries are estimated by optimizing an image energy function that can be edge‐ and/or region‐based . Active contour segmentation can be defined as parametric or geometric .…”

Section: Image Processingmentioning

confidence: 99%

“…However, a shortcoming of all active contours methods is that stopping criteria depend entirely on image information; thus, the evolving contour may leak through lower‐contrast boundaries . Recently, Wang et al proposed a hybrid geometric active contours method to overcome the problem of intensity inhomogeneity.…”

Section: Image Processingmentioning

confidence: 99%

Reconstruction of coronary circulation networks: A review of methods

et al. 2019

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Building anatomically accurate models of the coronary vascular system enables potentially deeper understandings of coronary circulation. To achieve this, (a) images at different levels of vascular network-arteries, arterioles, capillaries, venules, and veins-need to be obtained through suitable imaging modalities; and (b) from images, morphological and topological information needs to be extracted using image processing techniques. While there are several modalities that enable the imaging of large vessels, microcirculation imaging-capturing vessels having diameter lesser than 100 μm-has to date been typically confined to small regions of the heart. This spatially limited microcirculatory information has often been used within cardiac models, with the potentially erroneous assumption that it is representative of the whole organ. However, with the recent advancements in imaging and image processing, it is rapidly becoming feasible to acquire, process, and quantify microcirculation data at the scale of whole organ. In this review, we summarize the progress toward this goal followed through a presentation of the current state-of-the-art imaging and image processing techniques in the context of coronary microcirculation extraction, prominently but not exclusively, from small animals. K E Y W O R D Sconfocal imaging, coronary vascular system, deep learning, image processing, vessel extraction

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“…It is still a challenge to segment some complex medical images, because these medical images not only contain noise and intensity inhomogeneity but also have less obvious boundaries. To verify the efficiency and feasibility of our proposed IRLS-IS method on complex medical images, three existing methods, namely, the LSACM algorithm [51], the cross entropy-based model (CEM) [46] and the local hybrid image fitting model (LHIF) [77], are selected to conduct further comparative experiments, and the results are presented in Figure 7, where the original image Figure 7a is from [34,77] and Figure 7c,d were previously obtained by Wang et al [77]. Figure 7 presents experimental results of heart image segmentation with the four compared models.…”

Section: Segmentation Of Medical Imagesmentioning

confidence: 99%

“…Dice characterizes spatial overlaps between the segmented regions and the ground truth, and the formula of Dice [77] is expressed as…”

Section: Comparative Evaluationmentioning

confidence: 99%

An Image Segmentation Method Based on Improved Regularized Level Set Model

Sun

Meng

et al. 2018

Applied Sciences

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When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

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An active contour model based on local fitted images for image segmentation

Cited by 113 publications

References 44 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

Reconstruction of coronary circulation networks: A review of methods

An Image Segmentation Method Based on Improved Regularized Level Set Model

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