Comparative Study of Clustering Based Colour Image Segmentation Techniques

Chebbout, Samira; Merouani, Hayet Farida

doi:10.1109/sitis.2012.126

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…Authors in [7], have compared the effectiveness of three clustering methods involving RGB, HSV and CIE L*a*b* colour spaces and a variety of real colour images. The methods analysed were: K-Means clustering algorithm, Partitioning around Medoids method (PAM) and Kohonen's Self -Organizing Maps method (SOM).…”

Section: Energy-based Segmentation Methods Such Asmentioning

confidence: 99%

Intelligent technique for CT brain image segmentation

Mallick¹,

Satapathy

Mohanty

et al. 2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Image segmentation plays a vital role in medical imaging applications. It facilitates the delineation of anatomical structures and other regions of interest.Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and other imaging modalities provide an effective means for noninvasively mapping the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. In this paper, the brain image is considered for analysis and detection. Initially the region of interest is found, that helps to detect the particular content of the image and set the boundary of it. Fuzzy based clustering method is applied as an intelligent method for the image segmentation. For this purpose the thresholding using histogram is done. The final results are also compared for different clustering algorithms.

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Section: Energy-based Segmentation Methods Such Asmentioning

confidence: 99%

Intelligent technique for CT brain image segmentation

Mallick¹,

Satapathy

Mohanty

et al. 2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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“…Here they presented three unsupervised methods namely thresholding, K-means clustering and expectation maximization and evaluate their results. Chebbout, Samira, [6] revealed that proposed image segmentation technique has better accuracy than known ones. Vij, Sugandhi et al [8] discussed quantitative evaluation measures for color image segmentation predicated on these techniques.…”

Section: Ant Colony Optimizationmentioning

confidence: 98%

Ant Colony based Metaheuristic Technique for Color Image Segmentation

Kaur¹,

Mann²

2016

IJCA

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Image Segmentation is an essential procedure of image processing as well as knowing it. Mainly it's known due to the method of splitting the image into various elements of homogeneity. The goal of image segmentation is to make simpler presentation of an image into incredible which is meaningful as well as easy to understand. It is mainly utilized to know the location of objects, boundaries, lines etc in the digital images. Clustering technique is a method which shows the data set or pixels are replaced by cluster, pixels might be together because of the same color, texture etc. This paper represents the effect of mixed regions on image segmentation and color images contain maximum information for efficient image segmentation.

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“…Konsep dasar dari clustering adalah menemukan kesamaan antar data dan mengelompokkannya ke dalam satu kelompok. Proses pengelompokkan ini dilakukan dengan cara memaksimalkan kesamaan data di dalam kelompok dan meminimalkan kesamaan data antar kelompok [2]. Tingkat kesamaan ini dapat diukur menggunakan pengukuran jarak Euclidean, Cosine, Jaccard, dll.…”

Section: A Clusteringunclassified

Aerial Image Segmentation with Clustering Using Fireworks Algorithm

Arasy

Suyanto

Ramadhani

2019

IndoJC

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Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

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Comparative Study of Clustering Based Colour Image Segmentation Techniques

Cited by 16 publications

References 10 publications

Intelligent technique for CT brain image segmentation

Intelligent technique for CT brain image segmentation

Ant Colony based Metaheuristic Technique for Color Image Segmentation

Aerial Image Segmentation with Clustering Using Fireworks Algorithm

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