  In this paper, we propose sparse coding-based approaches for segmentation of
tumor regions from MR images. Sparse coding with data-adapted dictionaries has
been successfully employed in several image recovery and vision problems. The
proposed approaches obtain sparse codes for each pixel in brain magnetic
resonance images considering their intensity values and location information.
Since it is trivial to obtain pixel-wise sparse codes, and combining multiple
features in the sparse coding setup is not straightforward, we propose to
perform sparse coding in a high-dimensional feature space where non-linear
similarities can be effectively modeled. We use the training data from
expert-segmented images to obtain kernel dictionaries with the kernel K-lines
clustering procedure. For a test image, sparse codes are computed with these
kernel dictionaries, and they are used to identify the tumor regions. This
approach is completely automated, and does not require user intervention to
initialize the tumor regions in a test image. Furthermore, a low complexity
segmentation approach based on kernel sparse codes, which allows the user to
initialize the tumor region, is also presented. Results obtained with both the
proposed approaches are validated against manual segmentation by an expert
radiologist, and the proposed methods lead to accurate tumor identification.
