Back to Search Start Over

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

Authors :
Liu, Xingbin
Mei, Wenbo
Du, Huiqian
Source :
Medical & Biological Engineering & Computing. Sep2018, Vol. 56 Issue 9, p1565-1578. 14p. 1 Color Photograph, 10 Black and White Photographs, 2 Diagrams, 1 Chart, 1 Graph.
Publication Year :
2018

Abstract

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01400118
Volume :
56
Issue :
9
Database :
Academic Search Index
Journal :
Medical & Biological Engineering & Computing
Publication Type :
Academic Journal
Accession number :
131278133
Full Text :
https://doi.org/10.1007/s11517-018-1796-1