Brain tumor segmentation using cluster ensemble and deep super learner for classification of MRI

The Accurate segmentation and classification takes place a major role in the medical image processing to detect and locate the abnormal tissue region. In this, the three different types of brain magnetic resonance imaging (MRI) image source such as Type-1, Type-2 and Fluid attenuated inversion recov...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2021-10, Vol.12 (10), p.9939-9952
Main Authors: Ramya, P., Thanabal, M. S., Dharmaraja, C.
Format: Article
Language:English
Subjects:
Abnormalities
Accuracy
Algorithms
Artificial Intelligence
Brain
Brain cancer
Cellular automata
Classification
Clustering
Computational Intelligence
Engineering
Feature selection
Fourier transforms
Image processing
Image registration
Image segmentation
Magnetic resonance imaging
Medical imaging
Methods
Neural networks
Neuroimaging
Optimization
Original Research
Probabilistic models
Registration
Robotics and Automation
Tumors
User Interfaces and Human Computer Interaction
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Summary:The Accurate segmentation and classification takes place a major role in the medical image processing to detect and locate the abnormal tissue region. In this, the three different types of brain magnetic resonance imaging (MRI) image source such as Type-1, Type-2 and Fluid attenuated inversion recovery are combined by the image registration process to detect the clear region of the tumor tissue since, the region of interest identification in the single image data contains less key points to define it. In this paper, we implement the ensemble technique of image segmentation to segment the tumor region of the brain MRI image. For the segmentation process, the images are pre-processed by Laplacian cellular automata filtering method and segmented by ensemble of different clustering method such as K-means, fuzzy based clustering, self-organization map (SOM) and ensemble of Gaussian mixture model, K-means, SOM and their results are compared. This ensemble cluster label is consider as the segmented result and classify the abnormalities by using deep super learning method. The experimental results and the comparison charts defines the performance rate of proposed method comparing to the other state-of-art methods. The average accuracy for the proposed work is  98% in Ensemble 1 and  97% in Ensemble 2 methods for the BraTS brain image dataset.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-03390-8