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This paper presents a method based on a kernel dictionary learning algorithmfor segmenting brain tumor regions in magnetic resonance images (MRI). A set offirst-order and second-order statistical feature vectors are extracted frompatches of size 3 * 3 around pixels in the brain MRI scans. These featurevectors are utilized to train two kernel dictionaries separately for healthyand tumorous tissues. To enhance the efficiency of the dictionaries and reducetraining time, a correlation-based sample selection technique is developed toidentify the most informative and discriminative subset of feature vectors.This technique aims to improve the performance of the dictionaries by selectinga subset of feature vectors that provide valuable information for thesegmentation task. Subsequently, a linear classifier is utilized to distinguishbetween healthy and unhealthy pixels based on the learned dictionaries. Theresults demonstrate that the proposed method outperforms other existing methodsin terms of segmentation accuracy and significantly reduces both the time andmemory required, resulting in a remarkably fast training process.

MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning