An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding

Chakraborty, Rupak; Sushil, Rama; Garg, M. L.

doi:10.1007/s13369-018-3400-2

Cited by 53 publications

(28 citation statements)

References 44 publications

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“…In multi-level image segmentation, finding the optimal thresholds using optimization algorithms has been a popular method in the literature [40]- [42]. However, it is still challenging to set the optimal thresholds especially the number of thresholds is high.…”

Section: Multi-level Image Segmentationmentioning

confidence: 99%

A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

Yue

2020

IEEE Access

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The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm. INDEX TERMS Grasshopper optimization algorithm, invasive weed optimization, grouping strategy, random walk strategy, global optimization.

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Section: Multi-level Image Segmentationmentioning

confidence: 99%

A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

Yue

2020

IEEE Access

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“…The width of the δ (x) function is controlled by the parameter in (2). The C-V model is a powerful method to segment many types of images which are quite difficult to segment using gradient based or thresholding techniques.…”

Section: B Chan-vese (Cv) Modelmentioning

confidence: 99%

“…Image segmentation continues to be one of the basic and crucial problems in image processing and computer vision [1]. Object detection, object recognition, and image analysis are among the applications of image segmentation [2]- [4]. The purpose of image segmentation is to distinguish between the objects of interest and the background in an image.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

et al. 2020

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Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy. INDEX TERMS Active contours, bias field, image segmentation, intensity inhomogeneity, level set.

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“…Image segmentation is an important subject in related field of computer image analysis, image identification, object detection and other aspects [1]- [6]. The aim of segmentation is to separate a given image into different non-intersecting regions with specific properties such as intensities, colors and textures.…”

Section: Introductionmentioning

confidence: 99%

Active Contours Driven by Local and Global Region-Based Information for Image Segmentation

et al. 2020

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Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises. INDEX TERMS Active contour, image segmentation, signed pressure force, level set.

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An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding

Cited by 53 publications

References 44 publications

A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

Active Contours Driven by Local and Global Region-Based Information for Image Segmentation

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