2016
DOI: 10.1007/s11277-016-3420-8
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Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic Resonance (MRI) Images

Abstract: In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale… Show more

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Cited by 19 publications
(6 citation statements)
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“…Then, unlabeled information becomes labeled information based on the estimated features during the testing process. Several studies have utilized learning for brain tumor identification such as self-organized maps (SOM) [21], fuzzy c-means (FCM) [22], K-means [23], support vector machine (SVM), and artificial neural networks (ANN) [24], which are illustrated as follows:…”
Section: Machine Learning-based Techniquesmentioning
confidence: 99%
“…Then, unlabeled information becomes labeled information based on the estimated features during the testing process. Several studies have utilized learning for brain tumor identification such as self-organized maps (SOM) [21], fuzzy c-means (FCM) [22], K-means [23], support vector machine (SVM), and artificial neural networks (ANN) [24], which are illustrated as follows:…”
Section: Machine Learning-based Techniquesmentioning
confidence: 99%
“…Feng et al [13] proposed a novel segmentation fusion method which considered information from neighbouring pixels to boost image segmentation. Kim et al [14] in their research work proposed a method for detecting brain tumour using magnetic resonance (MR) images using a modified kmeans clustering technique and watershed algorithm. Similar works can also be cited from Singh et al [15], Abdel-Maksoud et al [16], and Maulik [17].…”
Section: Literature Survey Of Some Related Workmentioning
confidence: 99%
“…Under the asymptotic, the number of data points , while the bandwidth at a rate slower than n -1 . For both type of multivariate kernels, AMISE is measure is minimized by Epanechnikov kernel [12,11] having profile (10) Which produce the radially symmetric kernel (11) Here c d is a volume of the unit d-dimensional sphere. Note that the Epanechnikov profile is not differentiating at the boundary.…”
Section: B Mean-shiftmentioning
confidence: 99%