MRI–PET Medical Image Fusion Technique by Combining Contourlet and Wavelet Transform

2012 ◽  
pp. 145-151
Author(s):  
Ch. Hima Bindu ◽  
K. Satya Prasad
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Medical Image ◽  
Fusion Technique ◽  
Medical Image Fusion ◽  
Image Fusion Technique

Medical Image Fusion in Spatial and Transform Domain

2017 ◽  
pp. 281-300
Author(s):  
Alka Srivastava ◽  
Ashwani Kumar Aggarwal
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Medical Image ◽  
Medical Images ◽  
Spatial Domain ◽  
Transform Domain ◽  
Fusion Technique ◽  
Medical Image Fusion ◽  
Domain Fusion ◽  
Fused Image

Nowadays, there are a lot of medical images and their numbers are increasing day by day. These medical images are stored in the large database. To minimize the redundancy and optimize the storage capacity of images, medical image fusion is used. The main aim of medical image fusion is to combine complementary information from multiple imaging modalities (e.g. CT, MRI, PET, etc.) of the same scene. After performing medical image fusion, the resultant image is more informative and suitable for patient diagnosis. There are some fusion techniques which are described in this chapter to obtain fused image. This chapter presents two approaches to image fusion, namely spatial domain Fusion technique and transforms domain Fusion technique. This chapter describes Techniques such as Principal Component Analysis which is spatial domain technique and Discrete Wavelet Transform and Stationary Wavelet Transform which are Transform domain techniques. Performance metrics are implemented to evaluate the performance of image fusion algorithm.

scholarly journals Assessment of 3D Lumbosacral Vascular Anatomy for OLIF51 by Non-Enhanced MRI and CT Medical Image Fusion Technique

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1744
Author(s):  
Masakazu Nagamatsu ◽  
Sameer Ruparel ◽  
Masato Tanaka ◽  
Yoshihiro Fujiwara ◽  
Koji Uotani ◽  
...  
Keyword(s):  
Image Fusion ◽  
Vertebral Body ◽  
Medical Image ◽  
Interbody Fusion ◽  
Vascular Anatomy ◽  
Lumbar Interbody Fusion ◽  
Fusion Technique ◽  
Medical Image Fusion ◽  
The Mean ◽  
Image Fusion Technique

Study design: Prospective study. Objective: Medical image fusion can provide information from multiple modalities in a single image. The present study aimed to determine whether three-dimensional (3D) lumbosacral vascular anatomy could be adequately portrayed using a non-enhanced CT–MRI medical image fusion technique. Summary of Background Data: Lateral lumbar interbody fusion has gained popularity for the surgical treatment of adult spinal deformity (ASD). Oblique lumbar interbody fusion at L5–S1 (OLIF51) is receiving considerable attention as a method of creating good L5–S1 lordosis. Access in OLIF51 requires evaluation of the vascular anatomy in the lumbosacral region. Conventional imaging modalities need a contrast medium to describe the vascular anatomy. Methods: Participants comprised 15 patients with ASD or degenerative lumbar disease who underwent corrective surgery at our hospital between January 2020 and June 2021. A 3D vascular image with bony structures was obtained by fusing results from MRI and CT. We processed the merged image and measured the distance between left and right common iliac arteries and veins at two levels: the lower end of the L5 vertebral body (Window A) and the upper end of the S1 vertebral body (Window B). Results: The mean sizes of Window A and Window B were 29.7 ± 10.7 mm and 36.9 ± 10.3 mm, respectively. The mean distance from the bifurcation to the lower end of the L5 vertebra was 23.7 ± 10.9 mm. Coronal deviation of the bifurcation was, from center to left, 12.6 ± 12.3 mm, and the distance from the center of the L5 vertebral body to the bifurcation was 0.79 ± 7.3 mm. Only one case showed a median sacral vein (6.7%). Clinically, we performed OLIF51 in 12 of the 15 cases (80%). Conclusion: Evaluating 3D lumbosacral vascular anatomy using a non-enhanced MRI and CT medical image fusion technique is very useful for OLIF51, particularly for patients in whom the use of contrast medium is contraindicated.

scholarly journals Medical Image Fusion using BEMD and an Efficient Fusion Scheme

2018 ◽  
Author(s):  
M Mozaffarilegha ◽  
A Yaghobi Joybari ◽  
A Mostaar
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Images ◽  
Energy Operator ◽  
Fusion Rule ◽  
Fusion Technique ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Image Fusion Technique ◽  
Fusion Scheme

Background: Medical image fusion is being widely used for capturing complimentary information from images of different modalities. Combination of useful information presented in medical images is the aim of image fusion techniques, and the fused image will exhibit more information in comparison with source images.Objective: In the current study, a BEMD-based multi-modal medical image fusion technique is utilized. Moreover, Teager-Kaiser energy operator (TKEO) was applied to lower BIMFs. The results were compared to six routine methods.Methods: An image fusion technique using bi-dimensional empirical mode decomposition (BEMD), Teager-Kaiser energy operator (TKEO) as a local feature selection and HMAX model is presented. BEMD fusion technique can preserve much functional information. In the process of fusion, we adopt the fusion rule of TKEO for lower bi-dimensional intrinsic mode functions (BIMFs) of two images and HMAX visual cortex model as a fusion rule for higher BIMFs, which are verified to be more appropriate for human vision system. Integrating BEMD and this efficient fusion scheme can retain more spatial and functional features of input images.Results: We compared our method with IHS, DWT, LWT, PCA, NSCT and SIST methods. The simulation results and fusion performance show that the presented method is effective in terms of mutual information, quality of fused image (QAB/F), standard deviation, peak signal to noise ratio, structural similarity and considerably better results compared to six typical fusion methods.Conclusion: The statistical analyses revealed that our algorithm significantly improved spatial features and diminished the color distortion compared to other fusion techniques. The proposed approach can be used for routine practice. Fusion of functional and morphological medical images is possible before, during and after treatment of tumors in different organs. Image fusion can enable interventional events and can be further assessed.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

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