Title of Paper:Medical image fusion based on sparse representation of classified image patches

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Date of Publication:2017-04-01

Journal:BIOMEDICAL SIGNAL PROCESSING AND CONTROL

Included Journals:SCIE、EI

Volume:34

Page Number:195-205

ISSN No.:1746-8094

Key Words:Medical image fusion; Sparse representation; Patch classification; Online dictionary learning (ODL); Least angle regression (LARS)

Abstract:Medical image fusion is one of the hot research in the field of medical imaging and radiation medicine, and is widely recognized by medical and engineering fields. In this paper, a new fusion scheme for medical images based on sparse representation of classified image patches is proposed. In this method, first, the registered source images are divided into classified patches according to the patch geometrical direction, from which the corresponding sub-dictionary is trained via the online dictionary learning (ODL) algorithm, and the least angle regression (LARS) algorithm is used to sparsely code each patch; second, the sparse coefficients are combined with the "choose-max" fusion rule; Finally, the fused image is reconstructed from the combined sparse coefficients and the corresponding sub-dictionary. The experimental results showed that the proposed method outperforms other methods in terms of both visual perception and objective evaluation metrics. (C) 2017 Elsevier Ltd. All rights reserved.