Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain

Madhukumar, S.; Santhiyakumari, N.

doi:10.1016/j.ejrnm.2015.02.008

Cited by 81 publications

(23 citation statements)

References 7 publications

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“…In spite of their nearly equal performances, we recommend that KM can be used for its lower computing time cost as will be seen in Table 2 and higher number of correctly found clusters. Similar conclusions were reported by Madhukumar & Santhiyakumari (2015). For DS8 which is formed with two concave clusters and one circular cluster in middle of them, the failure rates were approximately 0.01 by all algorithms compared in this paper.…”

Section: Resultssupporting

confidence: 77%

“…FCM is an algorithm based on more iterative fuzzy calculations, so its execution was found comparatively higher as it is expected. Similar results were reported by Panda et al (2012) for Iris, Wine and Lens datasets; by Jipkate & Gohokar (2012) for segmentation of images; by Ghosh & Dubey (2013) for Iris dataset; by Bora & Gupta (2014) for Iris dataset; by Sivarathri & Govardhan (2014) for diabetes data; and by Madhukumar & Santhiyakumari (2015) for brain MR images data.…”

Section: Discussionsupporting

confidence: 74%

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Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Cebecí

Yildiz²

2015

JAI

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In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.

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Section: Resultssupporting

confidence: 77%

Section: Discussionsupporting

confidence: 74%

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Cebecí

Yildiz²

2015

JAI

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“…Gliomas arises from glial cells and infiltrate the surrounding tissues such as white matter fibres tracts with very rapid growth [2]. Accurate segmentation of brain tumor tissues from Brain MRI images is of profound importance in many clinical applications such as surgical planning and image-guided interventions [3]. Over the decades clustering algorithms in medical images have been topic of interest in researchers.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation

Baid¹,

Talbar²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Magnetic Resonance Imaging (MRI) is one of the widely used imaging modality for visualizing and assessing the brain anatomy and its functions in non-invasive manner. The most challenging task in analysis of brain MRI images is image segmentation. Automatic and accurate detection of brain tumor is one of the major areas of research in medical image processing. Accurate segmentation of brain tumor helps radiologists for precise treatment planning. In this paper results of one hard clustering algorithm i.e. K-means clustering and two soft clustering algorithm, Gaussian Mixture Model (GMM) and Fuzzy C-means (FCM) clustering are compared. These algorithms are tested on BRATS 2012 training database of High Grade and Low Grade Glioma tumors. Various evaluation parameters like Dice index, Jaccard index, Sensitivity, Specificity are calculated for all the algorithms and comparative analysis is carried out. Experimental results state that Fuzzy C-means clustering outperforms K-means and Gaussian Mixture Model algorithm for brain tumor segmentation problem.

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“…In this study, discrete cosine transform enabled, lifting wavelet transformation based image segmentation algorithm is developed for the detection of brain tumor from the MR images and morphological operation in extracting the brain tumor from the MR images. Brain tumor segmentation process involves separating the different tumor tissues like edema, necrosis and solid tumor from the normal brain tissues, such as white matter, gray matter, and cerebrospinal fluid with the help of magnetic resonance images or other imaging modalities [2]- [6]. Unlike other imaging modalities, an MR image is a non-invasive and good soft tissue contrast imaging modality.…”

Section: Introductionmentioning

confidence: 99%

DCT Enabled Lifting Wavelet Transformation based Image Analysis for Brain Tumor Detection and Extraction

Bahadure¹,

Ray²,

Thethi³

2017

IJET

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Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain

Cited by 81 publications

References 7 publications

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Comparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation

DCT Enabled Lifting Wavelet Transformation based Image Analysis for Brain Tumor Detection and Extraction

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