CAJ | 학술논문

针对脑部磁共振图像中白质、灰质和脑脊液的分割精度问题，提出一种融合稀疏表示和字典学习的图像分割方法。首先，利用基于块的输入数据来训练过完备字典；然后，根据学习到的字典获得最优稀疏表示的高维特征；最后，结合每个像素局部和非局部重构误差实现分割。在模拟和真实图像数据库上的实验结果表明，该方法能利用带有距离因子和稀疏因子的公式准确分割MR图像，在稳定性方面优于其他MR分割方法。
침대뇌부자공진도상중백질、회질화뇌척액적분할정도문제，제출일충융합희소표시화자전학습적도상분할방법。수선，이용기우괴적수입수거래훈련과완비자전；연후，근거학습도적자전획득최우희소표시적고유특정；최후，결합매개상소국부화비국부중구오차실현분할。재모의화진실도상수거고상적실험결과표명，해방법능이용대유거리인자화희소인자적공식준학분할MR도상，재은정성방면우우기타MR분할방법。
For segmentation accuracy problem of brain MR image in regard to white matter, gray matter and cerebrospinal fluid, we proposed an image segmentation method which fuses the sparse representation and dictionary learning.First, it trains the over-completed dictionary using block-based input data.Then, it obtains the high-dimensional feature represented by optimal sparse according to the dictionary learnt.Finally, it implements the segmentation by combining the local and nonlocal reconstruction errors of each pixel.Results of experiment on simulated and real image database show that the proposed method can use the formula with distance factor and sparse factor to accurately segment MR images, and is superior to other MR segmentation methods in terms of stability.