Brain Tumor Segmentation from Multi-Spectral Magnetic Resonance Image Data Using an Ensemble Learning Approach
| Autor: | Szabolcs Csaholczi, Laszlo Szilagyi, Agnes Gyorfi, Timea Fulop, Levente Kovács |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
Pixel Computer science business.industry Gabor wavelet Brain tumor Magnetic resonance imaging Pattern recognition Image segmentation medicine.disease Ensemble learning 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine Segmentation Artificial intelligence Brain tumor segmentation business 030217 neurology & neurosurgery |
| Zdroj: | SMC 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Popis: | The automatic segmentation of medical images represents a research domain of high interest. This paper proposes an automatic procedure for the detection and segmentation of gliomas from multi-spectral MRI data. The procedure is based on a machine learning approach: it uses ensembles of binary decision trees trained to distinguish pixels belonging to gliomas to those that represent normal tissues. The classification employs 100 computed features beside the four observed ones, including morphological, gradients and Gabor wavelet features. The output of the decision ensemble is fed to morphological and structural post-processing, which regularize the shape of the detected tumors and improve the segmentation quality. The proposed procedure was evaluated using the BraTS 2015 train data, both the high-grade (HG) and the low-grade (LG) glioma records. The highest overall Dice scores achieved were 86.5% for HG and 84.6% for LG glioma volumes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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