Automatic Brain Tumor Segmentation from MR Images via a Multimodal Sparse Coding Based Probabilistic Model

Yuhong Li, Qi Dou, Jinze Yu, Fucang Jia, Jing Qin, Pheng Ann Heng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)


Accurate segmentation of brain tumor from MR image is crucial for the diagnosis and treatment of brain cancer. We propose a novel automated brain tumor segmentation method based on a probabilistic model combining sparse coding and Markov random field (MRF). We formulate the brain tumor segmentation task as a pixel-wise labeling problem with regard to three classes: tumor, edema and healthy issue. For each class, dictionary learning is performed independently on multi-modality gray scale patches. Sparse representation is then extracted based on a joint dictionary which is constructed by combing the three independent dictionaries. Finally, we build the probabilistic model aiming to estimate maximum a posterior (MAP) probability by introducing the sparse representation into likelihood probability and prior probability using the Markov random field (MRF) assumption. Compared with traditional methods, which employed hand-crafted low level features to construct the probabilistic model, our model can better represent the characteristics of a pixel and its relation with neighbors based on the sparse coefficients obtained from the learned dictionary. We validated our method on the MICAAI 2012 BRATS challenge brain MRI dataset and achieved comparable or better results compared with state-of the-art methods.
Original languageEnglish
Title of host publicationProceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Number of pages4
ISBN (Electronic)9781467371452
Publication statusPublished - 16 Sep 2015
Externally publishedYes
Event5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 - Stanford, United States
Duration: 10 Jun 201512 Jun 2015


Conference5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Country/TerritoryUnited States


  • brain tumor segmentation
  • maximum a posterior
  • multi-modality
  • probabilistic model
  • sparse coding

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging

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