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Image Processing

  • Date Submitted: 04/15/2013 01:25 AM
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Madurai-625 009.

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Image Processing

                                                                  Submitted by,
                                                      S.T.Vinotha (B.Tech. – II year I.T.)
                                              B.Aarthy Krishna (B.Tech. – II year I.T.)

Bayesian Region growing Segmentation of Magnetic Resonance Images with Outlier detection for tumor identification

The segmentation of brain tumor from magnetic resonance (MR) images is a vital process for treatment planning and for studying the differences of healthy subjects and subjects with tumor. The process of automatically extracting tumor from MR images is a challenging process due to the lack of reliable ground truth. This paper proposes a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. Warping and geodesic interpolation on the diffusion tensors are used to simulate the displacement and the destruction of the white matter fibers. The principle behind the devised approach is segmenting fiber bundles from diffusion-weighted magnetic resonance images using a Bayesian locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those pixels which are locally connected to the fiber bundle. Rather than attempting to find large numbers of open curves or single fibers, which individually have questionable meaning, this method segments the full fiber bundle region. The strengths of this approach include its ease-of-use, computational speed, and applicability to a wide range of fiber bundles. The result is simulated multi-modal MRI with ground truth available as sets of probability maps....


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