In this paper a fully automatic method for segmenting MR images showing tumor, both mass-effect and infiltrating structures is presented. The proposed method uses UDWT and gabor wavelets. The proposed method uses Tl, T2 images and produces appreciative results even in the presence of noise. A multiresolution approach using undecimated wavelet transform is employed which allows the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands to remain at full size.Detection of tumor takes place in LL. The decomposition is carried up to two levels. Gabor filters are then applied to the wavelet approximations at all levels to obtain the characteristic texture features such as entropy, second to fourth central moments and coefficient of variation. A simple peak finding algorithm is used to determine the peaks out of array of these texture features. The corresponding filter outputs are compared to obtain an image containing minimum pixel values. This is given to the kmeans clustering algorithm which then produces the final segmented output. It is observed that the algorithm captures the features from the considered levels and produces an optimal segmentation. The proposed algorithm accurately locates the tumor tissue from surrounding brain tissue.
Skull stripping is an important image processing step in many neuroimaging studies. In this paper, a novel scheme based on a level sets representation of the geodesic active contour (GAC) is employed to detect the boundary of the skull. This approach is based on the relation between active contours and the computation of geodesics (minimal length curves). The contour is evolved from inside the MR image under the influence of geometric measures of the MR image. Before the application of GAC, the MR image is roughly done into two regions, brain and nonbrain. The centroid of the brain region is obtained which is used for drawing an ellipse situated well inside the brain region. This ellipse is the initial contour. After the model converges to a stable solution, the obtained mask is processed using morphological operators. This mask is then used to give the final segmented output.
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