  This paper presents a method based on a kernel dictionary learning algorithm
for segmenting brain tumor regions in magnetic resonance images (MRI). A set of
first-order and second-order statistical feature vectors are extracted from
patches of size 3 * 3 around pixels in the brain MRI scans. These feature
vectors are utilized to train two kernel dictionaries separately for healthy
and tumorous tissues. To enhance the efficiency of the dictionaries and reduce
training time, a correlation-based sample selection technique is developed to
identify the most informative and discriminative subset of feature vectors.
This technique aims to improve the performance of the dictionaries by selecting
a subset of feature vectors that provide valuable information for the
segmentation task. Subsequently, a linear classifier is utilized to distinguish
between healthy and unhealthy pixels based on the learned dictionaries. The
results demonstrate that the proposed method outperforms other existing methods
in terms of segmentation accuracy and significantly reduces both the time and
memory required, resulting in a remarkably fast training process.
