The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The
precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully au-
tomatic  brain  tumor  segmentation  method  based  on  kernel  sparse coding.  It  is  validated  with  3D  multiple-modality  magnetic
resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary
learning  is  used  to  extract  the  nonlinear  features  to  construct  five  adaptive  dictionaries  for  healthy  tissues,  necrosis,  edema,
non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original
MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is de-
veloped to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to
improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online
evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The
results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensi-
tivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor
(dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore,
it is a potential method in differentiation of healthy and pathological tissues.

关键词： Brain tumor segmentation,  Kernel method,  Sparse coding,  Dictionary learning