
Fusion of multi‐modal lumbar spine images using Kekre's hybrid wavelet transform
Author(s) -
Palkar Bhakti,
Mishra Dhirendra
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5609
Subject(s) - image fusion , artificial intelligence , computer science , computer vision , wavelet transform , modal , fusion , pattern recognition (psychology) , wavelet , image (mathematics) , linguistics , chemistry , philosophy , polymer chemistry
Image fusion is the process of merging multiple images to generate a single image called ‘fused image’ which is more informative than input images in terms of human perception and machine processing. In medical applications, images of the same or different modalities are fused to generate a new image which helps clinicians in reliable and accurate diagnosis. Fused image of mono‐modal medical images is used to see pre‐ and post‐operative results. Multi‐modal medical images are fused for treatment or surgical planning. In this study, the authors have focused on the fusion of lumbar spine images of two completely different modalities: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). CT provides bony details whereas MR provides soft tissue details. Since the two images are captured using two different machines, these images need to be strictly aligned with each other before fusing. Kekre's Hybrid wavelet transform (KHWT) is used to fuse registered images using combinations of six different orthogonal transforms with four different transform sizes. It is compared with five other fusion methods in qualitative and quantitative ways. The overall comparison indicates that the fused image generated using KHWT is better than input images in terms of content, quality and contrast.