Fast multilevel thresholding for image segmentation through a multiphase level set method

Dirami, Ahmed; Hammouche, Kamal; Diaf, Moussa; Siarry, Patrick

doi:10.1016/j.sigpro.2012.07.010

Cited by 99 publications

(36 citation statements)

References 30 publications

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“…The popular performance indicator, peak signal to noise ratio (PSNR) is used to compare segmentation results by threshold techniques of multilevel image [8][9][10]. PSNR criterion has been investigated as an evaluation criterion of quality of thresholding.…”

Section: Analysis Of Signal To Noise Ratiomentioning

confidence: 99%

Applying Chaotic Imperialist Competitive Algorithm for Multi-Level Image Thresholding Based on Kapur’s Entropy

Nejad¹,

Fartash²

2016

Adv. Sci. Technol. Res. J.

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Segmentation is one of the most important operations in image processing and computer vision. Normally, all image processing and computer vision applications are related to segmentation as a pre-processing phase. Image thresholding is one of the most useful methods for image segmentation. Various methods have been represented for image thresholding. One method is Kapur thresholding, which is based on maximizing entropy criterion. In this study, a new meta-heuristic algorithm based on imperialist competition algorithm was proposed for multi-level thresholding based on Kapur's entropy. Also, imperialist competitive algorithm is combined with chaotic functions to enhance search potency in problem space. The results of the proposed method have been compared with particle optimization algorithm and genetic algorithm. The findings revealed that the proposed method was superior to other methods.

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Section: Analysis Of Signal To Noise Ratiomentioning

confidence: 99%

Applying Chaotic Imperialist Competitive Algorithm for Multi-Level Image Thresholding Based on Kapur’s Entropy

Nejad¹,

Fartash²

2016

Adv. Sci. Technol. Res. J.

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“…The bi-level thresholding splits the image into two classes, whereas multilevel is used to segment complex images and it can produce multiple thresholds such as tri-level or quad-level, which split pixels into multiple similar parts based on intensity (Guo & Li, 2007) (Zhang & Wu, 2011). Bi-level thresholding can produce adequate outcomes in the case that the image contains only two principal gray levels, but the foremost restriction is the time-consuming computation which is often high when it is extended to multilevel thresholding (Dirami et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Kapur's entropy measures the homogeneity of the classes by using the maximization of the entropy. So, the selection of optimal thresholds in multilevel is a NP-hard problem (Marciniak et al, 2014), and it has been considered as a challenge over decades (Sarkar et al, 2013;Dirami et al, 2013). When the threshold number is small, classical techniques are suitable; but if this number increases, the accuracy of segmentation will be affected.…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Elaziz

Ewees

Hassanien

2017

Expert Systems with Applications

502

189

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“…The two-phase level set method can only partition images into two parts, making it unsuitable for multi-class segmentation in some medical applications. Recently, researchers have developed multi-phase level set methods [31,32], using two or more level set functions to define more than two membership functions, which makes it more practical in medical applications. Li et al [31] have combined bias field estimation with multi-phase level set method.…”

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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Fast multilevel thresholding for image segmentation through a multiphase level set method

Cited by 99 publications

References 30 publications

Applying Chaotic Imperialist Competitive Algorithm for Multi-Level Image Thresholding Based on Kapur’s Entropy

Applying Chaotic Imperialist Competitive Algorithm for Multi-Level Image Thresholding Based on Kapur’s Entropy

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

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

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