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.

翻译：本文提出了一种基于核字典学习算法的方法，用于分割磁共振图像中的脑肿瘤区域。从脑MRI扫描图像中像素周围3×3大小的图像块中提取一组一阶和二阶统计特征向量。这些特征向量被分别用于训练两个核字典，分别对应健康组织和肿瘤组织。为提高字典效率并减少训练时间，开发了一种基于相关性的样本选择技术，以识别最具信息量和判别性的特征向量子集。该技术旨在通过选择能为分割任务提供有价值信息的特征向量子集来提升字典性能。随后，基于学习到的字典，采用线性分类器区分健康与病变像素。结果表明，所提方法在分割精度上优于现有其他方法，并显著降低了所需时间和内存，从而实现了极快的训练过程。