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.

基于核字典学习算法的方法用于在磁共振图像中分割脑瘤区域，通过提取大小为 3*3 的图像块周围像素的一阶和二阶统计特征向量，训练两个分别用于健康和肿瘤组织的核字典，并通过基于相关性的样本选择技术，选择了最具信息和辨别力的特征向量子集，进而利用线性分类器根据学到的字典对健康和不健康像素进行区分，实验证明该方法在分割精度上优于其他现有方法，并显著减少了训练所需的时间和内存，从而实现了极快的训练过程。