Boundary-based image segmentation using binary level set method

Zhu, Guopu; Zhang, Shuqun; Zeng, Qingshuang; Wang, Changhong

doi:10.1117/1.2740762

Cited by 101 publications

(42 citation statements)

References 15 publications

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“…In order to smooth the level set function, Gaussian filter is used to regularize it. 7. When the evolution of the level set function has converged, the procedure stops.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…All the above ACMs are termed as edge-based models [7][8][9][10] because they utilize the image gradient as stopping criterion for the evolving curve.Edge-based models do not perform well in the presence of noise and in images with weak edges or without edges. In the case of a discrete gradient, the curve may pass through the edges because the function 0 ( ) g u ∇ never approaches zero at these points .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Global Threshold-Based Active Contour Model

Altarawneh¹,

Luo

Regan

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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In this paper, we propose a novel global threshold-based active contour model which employs a new edge-stopping function that controls the direction of the evolution and stops the evolving contour at weak or blurred edges. The model is implemented using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method has a selective local or global segmentation property. It selectively penalizes the level set function to be a binary function. This is followed by using a Gaussian function to regularize it. The Gaussian filters smooth the level set function and afford the evolution more stability. The contour could be initialized anywhere inside the image to extract object boundaries. The proposed method performs well when the intensities inside and outside the object are homogenous. Our method is tested on synthetic, medical and Arabiccharacters images with satisfactory results KEYWORDSImagesegmentation, Active contour,Geodesic active contour,C-V model,Level set method, ZAC model.

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“…In order to smooth the level set function, Gaussian filter is used to regularize it. 7. When the evolution of the level set function has converged, the procedure stops.…”

Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Global Threshold-Based Active Contour Model

Altarawneh¹,

Luo

Regan

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…Several studies show that the active contour models have been proved to be the most successful methods for image segmentation [6]. The basic idea of the active contour model is to develop a curves under some constraints to extract the desired object [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

Lestari

Madenda

Ernastuti

et al. 2017

IJECE

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Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics. Keyword:Breast lesion Level set Morphological operations Segmentation Ultrasound

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“…It is able to acquire closed contours of regions from an image, which helps partition of a medical image accurately. The authors in [28][29][30] considered a two-phase level set formulation, in which only one level set function was used to construct two membership functions to segment the image domain into two disjoint regions. The two-phase level set method can only partition images into two parts, making it unsuitable for multi-class segmentation in some medical applications.…”

Section: Introductionmentioning

confidence: 99%

Robust MRI abnormality detection using background noise removal with polyfit surface evolution

Liu

Cheng

Basu

et al. 2017

J Image Video Proc.

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Image segmentation plays a vital role in MRI abnormality detection. This paper presents a robust MRI segmentation method to outline potential abnormality blobs. Thresholding and boundary tracing strategies are employed to remove background noises, and hence, the ROIs in the whole process are set. Subsequently, a polyfit surface evolution is proposed to approximately estimate bias field, which makes segmentation robust to image noises. Simultaneously, customized initial level set functions are devised so as to detect subtle bright and dark blobs which are highly potential abnormality regions. The proposed method improves bias field estimation and level set method to acquire fine segmentation with low computational complexities. Analysis of experimental results and comparisons with existing algorithms demonstrates that the proposed method can segment weak-edged, low-resolution MR brain images, and its performance prevails in accuracy and effectiveness.

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Boundary-based image segmentation using binary level set method

Cited by 101 publications

References 15 publications

A Novel Global Threshold-Based Active Contour Model

A Novel Global Threshold-Based Active Contour Model

Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

Robust MRI abnormality detection using background noise removal with polyfit surface evolution

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