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Enhancing brain tumor segmentation for accurate tumor volume measurement is a challenging task due to the large variation of tumor appearance and shape, which makes it difficult to incorporate prior knowledge commonly used by other medical image segmentation tasks. In this paper, a novel idea of confidence surface is proposed to guide the segmentation of enhancing brain tumor using information across multi-parametric magnetic resonance imaging (MRI). Texture information along with the typical intensity information from pre-contrast T1 weighted (T1pre), post-contrast T1 weighted (T1post), T2 weighted (T2), and fluid attenuated inversion recovery (FLAIR) MRI images are used to train a discriminative classifier at pixel level. The classifier is used to generate a confidence surface, which gives a likelihood of each pixel being a tumor or non-tumor. The obtained confidence surface is then incorporated into two classical methods for segmentation guidance. The proposed approach was evaluated on 19 groups of MRI images with tumor and promising results have been demonstrated.

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