A segmentation method for brain tumor MR images based on multi-scale superpixel and kernel low-rank representation (KLRR) is proposed. First, homogeneous regions of the image are generated by the multi-scale superpixel segmentation, from which the spatial features are extracted to construct multi-scale superpixel kernels. Then, KLRR is adopted to model the high-dimensional feature space of the brain tumor image, and the representation coefficients in the model are solved by introducing the constructed multi-scale superpixel kernels. Finally, the optimal classification of samples is obtained by voting strategy, so as to extract necrosis, enhanced tumor and edema, respectively. Compared with a square window, the spatial features extracted based on multi-scale superpixel regions not only conform to the structural characteristics of brain tissues and tumors so as to maintain the boundaries better, but also can give more accurate descriptions of brain tissues and tumors of different sizes. In addition, KLRR combines the linear separability of the high-dimensional feature space induced by the kernel function with the advantages of low-rank representation (LRR) for describing the global structure, which improves the accuracy of the image representation. The experimental results on the BraTS data set show that, in addition to lower requirements for the size of the training samples, the segmentation accuracy of the proposed method under different indicators is better than that of the existing methods.
In this paper, we propose an improved method for simultaneous estimation of the bias field and segmentation of tissues
for magnetic resonance images, which is an extension of the method in. Firstly, the bias field is modeled as a linear
combination of a set of basis functions, and thereby parameterized by the coefficients of the basis functions. Then we
model the distribution of intensity in each tissue as a Gaussian distribution, and use the maximum a posteriori probability
and total variation (TV) regularization to define our objective energy function. At last, an efficient iterative algorithm
based on split Bregman method is used to minimize our energy function at a fast rate. Comparisons with other
approaches demonstrate the superior performance of this algorithm.