Medical image features extraction and fusion based on K-SVD.

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 over complete dictionary can be trained by K-singular value decomposition (K-SVD) algorithm and the sparse codes can be 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 over complete 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. [ABSTRACT FROM AUTHOR]