Open AccessComputational Effective Multimodal Medical Image Fusion in NSCT Domain
Author(s) -
G. Anand Kumar,
H. Devanna
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012003
Subject(s) - contourlet , image fusion , artificial intelligence , computer science , computer vision , image (mathematics) , consistency (knowledge bases) , fuse (electrical) , inverse , pattern recognition (psychology) , fusion , enhanced data rates for gsm evolution , mathematics , wavelet transform , engineering , linguistics , philosophy , geometry , wavelet , electrical engineering
Multimodal Medical Image Fusion provides comprehensive data in recent medical image diagnosis applications. This paper presents a novel Multimodal Medical Image fusion framework based on the Angular Consistency (AC) and Sub Band Adaptive Filtering (SAF). In the proposed model, the Non-subsampled Contourlet Transform (NSSCT) decomposes the source image pair into high frequency (HF) and low frequency (LF) sub bands. The LF bands are integrated with Angular consistency rule that can fuse the finer details which are very much helpful in medical image diagnosis. Further the HF bands are processed through sub band adaptive filtering such that the redundant information is filtered out and only significant information is preserved. At last the fused sub bands are processed for Inverse Non-subsampled Contourlet Transform to produce a fused image. Experimental evaluation is accomplished through a real time image data set and the performance is measured through Edge Based Similarity index, Local Quality Index and Computational time. The obtained results show that the proposed system achieves a satisfactory results compared to the conventional methods.