A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization

Mirghasemi, Saeed; Rayudu, Ramesh; Zhang, Mengjie

doi:10.1109/ivcnz.2013.6727045

Cited by 15 publications

(9 citation statements)

References 30 publications

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“…Preetha et al proposed an automatic seeded region growing algorithm, in which seed points were selected based on the similarity and the Euclidean distance of a pixel to its neighbors [13]. Mirghasem et al proposed a new image segmentation algorithm based on modified seeded region growing, in which seed points were selected based on particle swarm optimization algorithm [14]. But instead seed point selection is affected by particular technique limitation and increases the computation overhead; consequently it is still a challenge for researchers.…”

Section: Introductionmentioning

confidence: 99%

A Non-seed-based Region Growing Algorithm for High Resolution Remote Sensing Image Segmentation

Wang

Long²,

Liu³

2015

Lecture Notes in Computer Science

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One of the indispensable prerequisites for high resolution remote sensing image interpretation and processing is successful image segmentation. The algorithm presented in this paper aims for a high efficient image segmentation applicable and adaptable to high resolution remote sensing images. This is achieved by a non-seed-based region growing, which constructs neighbor pairwise pixel stack instead of depending on any seed points. The stack is constructed in increasing order of neighbor pairwise pixel spectral difference which is computed based on 4-connexity. The proposed algorithm carries out region growing according to the merging criterion (i.e. grow formula) and traversal of the stack. We apply the proposed and conventional region growing algorithms to two data sets of ZiYuan-3 (ZY-3) high resolution remote sensing images and analyze the segmentation results based on Carleer evaluation method that manifests high efficient segmentation of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

A Non-seed-based Region Growing Algorithm for High Resolution Remote Sensing Image Segmentation

Wang

Long²,

Liu³

2015

Lecture Notes in Computer Science

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“…Lu et al [23] Utilized Quasi-Monte Carlo strategy to improve conventional RG technique, the improved procedure upgrades the proficiency of choosing the right seed focuses and the improved RG rules better suits the liver division. Seeded RG (SRG) in view of PSO is presented in [24]. The strategy could be considered as one of the methodologies which present another support of PSO for image segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…With this procedure, Evolutionary Algorithms (EA) are considered with improvements in points such as issue and determination independence, nearby optima evasion, and straightforwardness. Many of these algorithms for example, Firefly Algorithm (FA) [53], GA [54], PSO [24], ABC [55], ALO [56], GWO [57,58], ACO [59] and others have been implemented in image segmentation. Among them, in the study using ALO, Mostafa et al [56] proposed a methodology for liver segmentation based on ALO.…”

Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

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“…However, the major problem of region growing algorithm is to find the optimal solution (Mirghasemi et al, 2013) by selecting the seed point. This seed point can be evaluated using particle swarm optimisation to get the best solution among different pixels.…”

Section: Seeded Region Growing Segmentation Using Psomentioning

confidence: 99%

Particle swarm optimisation K-means clustering segmentation of foetus ultrasound image

Parasar

Rathod²

2017

IJSISE

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The purpose of medical image segmentation is to extract information such as volume, shape, motion of organs for detecting abnormalities from the medical image for improvement and fast diagnosis. In this paper, a segmentation algorithm has been implemented for foetus ultrasound image by particle swarm optimisation (PSO) K-means clustering algorithm with fuzzy filter. Impulsive noise inherent in ultrasound image has been removed using fuzzy filter. Then, PSO K-means clustering segmentation method is applied for partitioning foetus ultrasonic images into multiple segments, which applies an optimal suppression factor for the perfect clustering in the specified data set. Experimental results show that the proposed algorithm outperforms other segmentation algorithms like seeded region growing using PSO, fuzzy C-means and watershed in terms of segmentation accuracy for speckle noise added to foetus ultrasound medical images.

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A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization

Cited by 15 publications

References 30 publications

A Non-seed-based Region Growing Algorithm for High Resolution Remote Sensing Image Segmentation

A Non-seed-based Region Growing Algorithm for High Resolution Remote Sensing Image Segmentation

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Particle swarm optimisation K-means clustering segmentation of foetus ultrasound image

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