Brain Tumour Segmentation Method Based on Supervoxels and Sparse Dictionaries
This paper presents an automatic method for brain tumour segmentation from magnetic resonance images. The method uses the feature vectors obtained by an efficient feature encoding approach which combines the advantages of the supervoxels and sparse coding techniques. Extremely Randomized Trees (ERT) algorithm is trained using these feature vectors to detect the whole tumour and for multi-label classification of abnormal tissues. A Conditional Random Field (CRF) algorithm is implemented to delimit the region where the brain tumour is located. The obtained predictions of the ERT are used to estimate probability maps. The probability maps of the images and the Euclidean distance between the feature vectors of neighbour supervoxels define the conditional random field energy function. The minimization of the energy function is performed via graph cuts. The proposed methods are evaluated on real patient data obtained from BraTS 2018 challenge. Results demonstrate that proposed method achieves a competitive performance on the validation dataset using Dice score is: 0.5719, 0.7992 and 0.6285 for enhancing tumuor, whole tumour and tumour core respectively. The achieved performance of this method on testing set using Dice score is: 0.5081, 0.7278 and 0.5778 for enhancing tumuor, whole tumour and tumour core respectively.
KeywordsBrain tumour segmentation Sparse coding techniques Supervoxels
The first author’s research was partially supported by a CONACyT postdoctoral grant and CONACyT Research Grant 256126. The first author would also like to thank the University of Exeter for its hospitality. We thank to the organizing committee of MICCAI BraTS 2018 for help us in the evaluation of the performance of our method.
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