Semi-automatic Segmentation of MRI Brain Tumor

Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues can not be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A semi-automatic method has been developed for segmentation of brain tumor from MR images. Segmentation of 3-D tumor structures from magnetic resonance images (MRI) is a very challenging problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over the conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside the tumor and can leak through weak or missing boundary parts. Replacing the constant propagation term by a statistical force overcomes these limitations and results in a convergence to a stable solution. Using MR images presenting tumors, probabilities for background and tumor regions are calculated from a pre- and post-contrast difference image and mixture modeling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the tumor boundaries. Results on two cases presenting different tumors with significant shape and intensity variability show that the method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert radiologist.