An Intensity-Texture model based level set method for image segmentation

Min, Hai; Wei, Jia; Wang, Xiaofeng; Zhao, Yang; Hu, Rong-Xiang; Luo, Yanlin; Xue, Feng; Lu, Jingting

doi:10.1016/j.patcog.2014.10.018

Cited by 88 publications

(53 citation statements)

References 42 publications

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“…Level-set methods are widely used in image segmentation [27][28][29], and has recently been applied to detect abnormality in OCT en face images [30]. Level-set method represents the boundary of interest in image I as contour φ = 0 (i.e.…”

Section: Fuzzy Level-set Methodsmentioning

confidence: 99%

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Wang

Zhang

Pechauer

et al. 2016

Biomed. Opt. Express

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Abstract:We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.

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Section: Fuzzy Level-set Methodsmentioning

confidence: 99%

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Wang

Zhang

Pechauer

et al. 2016

Biomed. Opt. Express

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“…Recently, an intensity-texture model [21] was introduced to segment texture images. However only two-phase texture images can be segmented.…”

Section: Discussionmentioning

confidence: 99%

“…R(u(x)) is referred to as a regularisation term (see literature [18] for more details). Although this model has been well applied to some kinds of medical images with inhomogeneity and noise under high contrast between the foreground and the background, it does not consider the clustering variance, which may cause unsatisfied segmentation for the images with severe intensity inhomogeneity [21]. The time complexity of the BCLBF model: in every iteration, the computing of the BCLBF model includes the updating of the region parameters, the level set, the bias field term and its two gaussian kernel convolutions, as well as the regularised term which contains the curvature term and the penalty term.…”

Section: The Bclbf Modelmentioning

confidence: 99%

Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation

et al. 2017

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This paper proposes a novel active contour model called weighted kernel mapping (WKM) model along with an extended watershed transformation (EWT) method for the level set image segmentation, which is a hybrid model based on the global and local intensity information. The proposed EWT method simulates a general spring on a hill with a fountain process and a rainfall process, which can be considered as an image pre-processing step for improving the image intensity homogeneity and providing the weighted information to the level set function. The WKM model involves two new energy functionals which are used to segment the image in the low dimensional observation space and the higher dimensional feature space respectively. The energy functional in the low dimensional space is used to demonstrate that the proposed WKM model is right in theory. The energy functional in the higher dimensional space obtains the region parameters through the weighted kernel function by utilising mean shift technique. Since the region parameters can better represent the values of the evolving regions due to the better image homogeneity, the proposed method can more accurately segment various types of images. Meanwhile, by adding the weighted information, the level set elements can be updated faster and the image segmentation can be achieved with fewer iterations. Experimental results on synthetic, medical and natural images show that the proposed method can increase the accuracy of image segmentation and reduce the iterations of level set evolution for image segmentation.

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“…One of the most widely used fuzzy clustering algorithms is the FCM Algorithm [37]. This can be used in many fields such as pattern recognition, fuzzy identification, and feature extraction.…”

Section: Fcmmentioning

confidence: 99%

Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering

Nithila

Kumar

2016

Alexandria Engineering Journal

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The aim of this paper was to develop a region based active contour model and Fuzzy C-Means (FCM) technique for segmentation of lung nodules. Ultimately, detection and assisted diagnosis of nodules at earlier stage increase the mortality rate. Among many imaging modalities, Computed Tomography (CT) is being the most sought because of its imaging sensitivity, high resolution and isotropic acquisition in locating the lung lesions. The proposed methodology focuses on acquisition of CT images, reconstruction of lung parenchyma and segmentation of lung nodules. Reconstruction of parenchyma can be employed using selective binary and Gaussian filtering with new signed pressure force function (SBGF-new SPF) and clustering technique was used for nodule segmentation. Comparative experiments demonstrate the advantages of the proposed method in terms of decreased error rate and increased similarity measure. Ó 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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An Intensity-Texture model based level set method for image segmentation

Cited by 88 publications

References 42 publications

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation

Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering

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