Automatic segmentation of brain tumors from MR images using undecimated wavelet transform and gabor wavelets

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 T1, 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.