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The segmentation of brain tumor using Magnetic Resonance Image (MRI) plays an important role in the medical image process. This paper presents a comprehensive survey on brain tumor methods and technology using MRI images. Generally, brain tumor segmentation methods can divided into two main categories, spatial continuous and spatial discrete methods. Several methods, techniques, related advantage and weakness will be described and discussed. The evaluation measures are mentioned and the qualities of different method focus on the methods that were applied on the standard data sets. The efficient and stably brain tumor segmentation is still a challenging task for the unpredictable appearance and shape of the brain tumor.

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Biological images with significant intensity inhomogeneity are considerably difficult for the tissue segmentation. To overcome the difficulties caused by the intensity inhomogeneity, this study presents a variational level set method to simultaneous bias field estimation and tissue segmentation for images in the presence of intensity inhomogeneity. An energy function is defined in terms of two data fitting terms which incorporate the local clustering properties into the global region information. First, depended on the observed image mode, the local cluster property based on the observed signal is simplified to a criterion function which is similar to the Mumford‐Shah model. The local criterion energy is then integrated with a global region measure, which is based on intensity difference of the true signal. The energy is minimized in a variational level set formulation with a regularity term, thus avoiding the expensive computation of the level set reinitialization and keeping the curve close to the signal distance function. Experiment results on biological images show desirable performance and demonstrate the effectiveness of the proposed algorithm.