Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

Ng, Howard P.; Ong, Sim‐Heng; Foong, Kelvin Weng Chiong; Goh, Poh Sun; Nowinski, Wiesław L.

doi:10.1109/ssiai.2006.1633722

Cited by 322 publications

(169 citation statements)

References 14 publications

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“…Clustering can be applied to a wide range of domains like: marketing [1] (market analysis and recommendations, methodological weaknesses), medicine [2] (medical image segmentation), e-business [3] (comments analysis on news portal) or e-learning [4][5] (prediction of students' academic performance).…”

Section: Introductionmentioning

confidence: 99%

An Optimized Version of the K-Means Clustering Algorithm

Poteras¹,

Mihăescu²,

Mocanu³

2014

Annals of Computer Science and Information Systems

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Abstract-This paper introduces an optimized version of the standard K-Means algorithm. The optimization refers to the running time and it comes from the observation that after a certain number of iterations, only a small part of the data elements change their cluster, so there is no need to re-distribute all data elements. Therefore the implementation proposed in this paper puts an edge between those data elements which won't change their cluster during the next iteration and those who might change it, reducing significantly the workload in case of very big data sets. The prototype implementation showed up to 70% reduction of the running time.

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Section: Introductionmentioning

confidence: 99%

An Optimized Version of the K-Means Clustering Algorithm

Poteras¹,

Mihăescu²,

Mocanu³

2014

Annals of Computer Science and Information Systems

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“…This clustering is convergent and its aim is to optimize the partitioning decisions based on a user-defined initial set of clustering that is updated after each iteration [47]. It produces hard segmentation by restricting a pixel's membership exclusively to one class [1], [24]. It is a simple clustering method with low computational complexity as compared to FCM [27].…”

Section: Standard K-means Algorithmmentioning

confidence: 99%

“…Various thresholding based algorithms for MR segmentation have been proposed in [19]- [23]. K-means clustering, an unsupervised method produces hard segmentation by restricting a pixel's membership exclusively to one class [1], [24]. K-means is suitable for MIS as the number of clusters (k) is usually known for images of particular region of human anatomy [25].K-means has been used extensively for segmentation of MR images.…”

Section: Introductionmentioning

confidence: 99%

“…K-means is suitable for MIS as the number of clusters (k) is usually known for images of particular region of human anatomy [25].K-means has been used extensively for segmentation of MR images. Various K-means based algorithms for MRI segmentation have been presented in [13], [24]- [28]. Morphology is used to transform an image into another image by eliminating undesirable features.…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

Sohi¹,

Kaur²,

Gupta³

2012

IJCA

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Aim of this paper is to develop an efficient fuzzy c-mean based segmentation algorithm to extract tumor region from MR brain images. First, cluster centroids are initialized through data analysis of tumor region, which optimizes the standard fuzzy cmean algorithm. Next, reconstruction based morphological operations are applied to enhance its performance for brain tumor extraction. The results show that simple fuzzy c-mean could not segment the region of interest properly, whereas enhanced algorithm effectively extracts the tumor region. From comparison with existing segmentation methods, enhanced fuzzy c-mean algorithm emerges as the most effective algorithm for extracting region of interest.

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“…K-means clustering is one of the most popular statistical clustering techniques used in segmentation of medical images [66,72,94,[108][109][110]. The name K-means originates from the means of the k clusters that are created from n objects.…”

Section: Adaptation Of C-means To Rough Set Theorymentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

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Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

Cited by 322 publications

References 14 publications

An Optimized Version of the K-Means Clustering Algorithm

An Optimized Version of the K-Means Clustering Algorithm

Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

Computational Intelligence in Multimedia Processing: Foundation and Trends

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