2015
DOI: 10.1007/s13246-015-0352-7
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Supervised segmentation of MRI brain images using combination of multiple classifiers

Abstract: Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of comb… Show more

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Cited by 20 publications
(9 citation statements)
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“…For the classification of brain tumor regions, the Magnetic Resonance Imaging (MRI) is commonly used. MRI can be considered as one of the most successful diagnostic tools for capturing the soft-tissues and visualizing the various organs in body [9]. As the MRI captures the organs in three dimensions, it helps much in analyzing effects occurring in inner organs.…”
Section: Introductionmentioning
confidence: 99%
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“…For the classification of brain tumor regions, the Magnetic Resonance Imaging (MRI) is commonly used. MRI can be considered as one of the most successful diagnostic tools for capturing the soft-tissues and visualizing the various organs in body [9]. As the MRI captures the organs in three dimensions, it helps much in analyzing effects occurring in inner organs.…”
Section: Introductionmentioning
confidence: 99%
“…In the last few decades, numerous segmentation techniques with varying degree of accuracy and level of complexity have been developed [2]. Segmentation of MRI images is a primary step in most applications of medical image processing [9]. Segmentation is a procedure to separate similar portions of images showing resemblance in different features, like shape, size, color, etc [22].…”
Section: Introductionmentioning
confidence: 99%
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“…Therefore, a great deal of efforts has been made in order to automate MRI image segmentation (Ahmadvand and Daliri ). For instance, Ahmadvand et al proposed a combination of multiple classifier based method, named Dynamic Classifier Selection‐ Dynamic Local Training Local Tanimoto Index, for MRI brain segmentation into three main tissues (Ahmadvand et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, MRF (Markov Random Field) method powerfully employed, but this method has a high computational cost. Clustering is the main process, here it will act as an initial segmentation and approximation of MRF parameters, after that MRF method is used to apply in post‐processing step for smoothness of different segments, for each and every image the clustering can be done through two different models like Gaussian mixture model (GMM) and Fuzzy C‐Means (FCM) which is the main categories for MRI image segmentation . Many recent researchers concentrated on brain tumor issue this can be able to identify the changes in shape, volume and regional distribution of brain tissue.…”
Section: Introductionmentioning
confidence: 99%