Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion

• Published in: IEEE transactions on biomedical engineering Vol. 59; no. 12; pp. 3450 - 3459
• Main Authors: Li, Shutao, Yin, Haitao, Fang, Leyuan
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D medical image denoising; Algorithms; Applied sciences; Biological and medical sciences; Biomedical imaging; Brain - anatomy & histology; Computerized, statistical medical data processing and models in biomedicine; Diagnostic Imaging - methods; Dictionaries; dictionary learning; Exact sciences and technology; Female; graph regularization; Graphs; group sparse representation; Humans; Image denoising; Image processing; Imaging, Three-Dimensional - methods; Information theory; Information, signal and communications theory; Learning; Learning systems; Medical; medical image fusion; Medical management aid. Diagnosis aid; Medical sciences; Middle Aged; Models, Theoretical; Noise measurement; Regularization; Representations; Signal processing; Sparse matrices; Sparsity; Studies; Teaching methods; Telecommunications and information theory; temporal regularization; Three dimensional; 3-D medical image denoising; Dictionaries; temporal regularization; Image processing; Noise reduction; group sparse representation; graph regularization; dictionary learning; medical image fusion; Learning; Acquisition process; Information processing; Signal processing; Data fusion; Medical imagery; Sparse representation; Tridimensional image; Image fusion; Biomedical engineering
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2012.2217493

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.