基于K-SVD的医学图像特征提取和融合

Medical image features extraction and fusion based on K-SVD

医学图像融合能够综合两种不同模态图像的信息,从而帮助医生做出准确的诊断和治疗.利用稀疏表示进行图像的特征提取和融合.首先由原始图像组成联合矩阵,通过K-SVD算法得出这个联合矩阵的冗余字典并求出联合矩阵的稀疏编码;然后将稀疏系数作为图像特征,并采用最大化选择算法合并相对应图像块的稀疏编码;最后通过稀疏编码和冗余字典得到融合图像.与3种流行的融合算法比较,结果表明所提算法在无噪声和有噪声的情况下都具有很好的性能.

Medical image fusion can integrate the information of two different modal images, which can provide doctors with accurate diagnosis and treatment. The image features are extracted and fused by sparse representation. Firstly, all source images are combined into a joint-matrix. The overcomplete dictionary can be trained by K-singular value decomposition （K-SVD） algorithm and the sparse codes can he acquired by joint-matrix. Secondly, the sparse codes which are considered as image features are combined with the choosing max fusion rule. Finally, the fused image is reconstructed from the combined sparse codes and the overcomplete dictionary. Compared with three state-of-the-art algorithms, the results show that the proposed method has better fusion performance in both noiseless and noisy situations.