Qualitative and Quantitave Analysis of Six Image Fusion Methodologies and Their Application to Medical Imaging

2011 ◽  
Author(s):  
Seyyed Adel Alavi Fazel ◽  
Yaniv Gal ◽  
Zhengyi Yang ◽  
Viktor Vegh
Keyword(s):  
Medical Imaging ◽  
Image Fusion

scholarly journals Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

2020 ◽  
Vol 10 (3) ◽  
pp. 1171 ◽  
Author(s):  
Chengxi Li ◽  
Andrew Zhu
Keyword(s):  
Liver Disease ◽  
Radiofrequency Ablation ◽  
Medical Imaging ◽  
Image Fusion ◽  
Liver Tumors ◽  
Imaging Modality ◽  
Fusion Technology ◽  
Tumor Treatment ◽  
Anatomical Imaging ◽  
Functional Images

With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

scholarly journals Video Distortion Using Region based DT-CWT Fusion

2021 ◽  
pp. 313-321
Author(s):  
V. Supraja ◽  
Y. Yashaswini ◽  
Vajjala Sravani ◽  
Valluru Sreevani ◽  
Poll Sharada Reddy
Keyword(s):  
Remote Sensing ◽  
Medical Imaging ◽  
Image Registration ◽  
Image Fusion ◽  
Police Work

Restoring a scene distorted by a region turbulence could be a difficult drawback in video police work. An image registration allows the geometric alignment of 2 pictures and is wide utilized in varied applications within the fields of remote sensing, a medical imaging and laptop vision. During this paper, we tend to propose a unique methodology for mitigating the consequences of a region distortion on discovered pictures. a region of an interest (ROI) for every frame is taken, to extract correct detail regarding objects behind the distorting layer. An easy and economical frame choice methodology is planned to pick informative ROIs, solely from smart quality frames. Every ROI ought to be register to cut back the distortion. The house variable drawback will be solved by image fusion mistreatment complicated ripple remodel. Finally distinction sweetening is applied.

scholarly journals A Hybrid Image Fusion Algorithm for Medical Applications

2021 ◽  
Author(s):  
Appari Geetha Devi ◽  
Surya Prasada Rao Borra ◽  
Kalapala Vidya Sagar
Keyword(s):  
Medical Imaging ◽  
Image Fusion ◽  
Ct Scan ◽  
Low Dose ◽  
Structural Data ◽  
Fusion Algorithm ◽  
Element Analysis ◽  
Low Dose Ct ◽  
Initial Stage ◽  
The Brain

The main objective of medical imaging is to get a extremely informative image for higher designation. One modality of medical image cannot offer correct and complete data in several cases. In brain medical imaging, resonance Imaging (MRI) image shows structural data of the brain with none useful information, wherever as pc imaging (CT) image describes useful data of the brain however with low spatial resolution particularly with low dose CT scan, that is helpful to scale back the radiation impact to physique. Within the field of diagnosing, Image fusion plays a really very important role. Fusing the CT and tomography pictures provides a whole data concerning each soft and exhausting tissues of the physique. This paper proposes a 2 stage hybrid fusion formula. Initial stage deals with the sweetening of a coffee dose CT scan image exploitation totally different image sweetening techniques viz., bar graph Equalization and adaptation bar graph deed. Within the second stage, the improved low dose CT scan image is united with tomography image exploitation totally different fusion algorithms viz., distinct rippling rework (DWT) and Principal element Analysis (PCA). The projected formula has been evaluated and compared exploitation totally different quality metrics.

Effective and Accurate Diagnosis Using Brain Image Fusion

2020 ◽  
pp. 197-217
Author(s):  
Sivakumar Rajagopal ◽  
Babu Gopal
Keyword(s):  
Medical Imaging ◽  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Processing Technique ◽  
Discrete Wavelet ◽  
Brain Images ◽  
Brain Image ◽  
Local Means ◽  
Tissue Abnormalities

Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.

scholarly journals Pixel level image fusion for medical imaging: an energy minimizing approach

2012 ◽  
Author(s):  
Brandon Miles ◽  
Max W. K. Law ◽  
Ismail Ben-Ayed ◽  
Greg Garvin ◽  
Aaron Fenster ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Image Fusion

An Efficient FPGA Architecture with High-Performance 2D DWT Processor for Medical Imaging

2016 ◽  
Vol 25 (09) ◽  
pp. 1650110 ◽  
Author(s):  
S. P. Valan Arasu ◽  
S. Baulkani
Keyword(s):  
Medical Imaging ◽  
Low Power ◽  
Image Fusion ◽  
High Performance ◽  
Large Scale ◽  
Medical Images ◽  
Discrete Wavelet ◽  
Significant Information ◽  
Low Area ◽  
Fpga Architecture

Medical image fusion is the process of deriving vital information from multimodality medical images. Some important applications of image fusion are medical imaging, remote control sensing, personal computer vision and robotics. For medical diagnosis, computerized tomography (CT) gives the best information about denser tissue with a lesser amount of distortion and magnetic resonance image (MRI) gives the better information on soft tissue with little higher distortion. The main scheme is to combine CT and MRI images for getting most significant information. The need is to focus on less power consumption and less occupational area in the implementations of the applications involving image fusion using discrete wavelet transform (DWT). To design the DWT processor with low power and area, a low power multiplier and shifter are incorporated in the hardware. This low power DWT improves the spatial resolution of fused image and also preserve the color appearance. Also, the adaptation of the lifting scheme in the 2D DWT process further improves the power reduction. In order to implement this 2D DWT processor in field-programmable gate array (FPGA) architecture as a very large scale integration (VLSI)-based design, the process is simulated with Xilinx 14.1 tools and also using MATLAB. When comparing the performance of this low power DWT and other available methods, this high performance processor has 24%, 54% and 53% of improvements on the parameters like standard deviation (SD), root mean square error (RMSE) and entropy. Thus, we are obtaining a low power, low area and good performance FPGA architecture suited for VLSI, for extracting the needed information from multimodality medical images with image fusion.

scholarly journals The Generalised Image Fusion Toolkit (GIFT)

2006 ◽  
Author(s):  
Dan Mueller
Keyword(s):  
Medical Imaging ◽  
Image Fusion ◽  
Worked Examples ◽  
Source Image ◽  
Discrete Wavelet ◽  
Human Visual Perception ◽  
Quadrature Mirror Filter ◽  
Multi Scale ◽  
Fused Image ◽  
Fusion Methods

Image fusion provides a mechanism to combine multiple images into a single representation to aid human visual perception and image processing tasks. Such algorithms endeavour to create a fused image containing the salient information from each source image, without introducing artefacts or inconsistencies. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geoscience, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualisation of multi-modal datasets.This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a ‘plug-and-play’ environment for the construction of n-D multi-scale image fusion methods. We give a brief overview of the toolkit design and demonstrate how to construct image fusion algorithms from low-level components (such as multi-scale methods and feature generators). A number of worked examples for medical applications are presented in Appendix A, including quadrature mirror filter discrete wavelet transform (QMF DWT) image fusion.

scholarly journals Integrating image fusion with nanoparticle contrast agents for diagnosis: a review

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Eric Naab Manson ◽  
Francis Hasford ◽  
Stephen Inkoom ◽  
Ahmed Mohammed Gedel
Keyword(s):  
Patient Safety ◽  
Medical Imaging ◽  
Radiation Dose ◽  
Contrast Agents ◽  
Image Fusion ◽  
Single Image ◽  
Promising Technique ◽  
Main Text ◽  
Different Types

Abstract Background As newer technologies in the field of medical imaging continue to expand, development of unique techniques for optimizing image quality and minimizing radiation dose becomes very necessary for improve diagnosis of pathologies and patient safety. Different types of medical imaging devices have been developed for specific diagnostic purposes. Main text This article provides a brief overview on the need for co-registration of some medical images into a single image (image fusion), advantages of some nanoparticle contrast agents in medical imaging, and a discussion of present and future role of integrating image fusion with nanoparticle contrast agents in diagnosis. Conclusion The use of nanoparticle contrast agents together with image fusion is a promising technique in future medical imaging as is likely to reveal pathologies of ≤ 1 nm sizes.

1528: Improved Sentinel Node Detection and Pre-Operative Planning in Prostate Cancer by Image Fusion of CT/MRI and Spect

2005 ◽  
Vol 173 (4S) ◽  
pp. 414-414
Author(s):  
Frank G. Fuechsel ◽  
Agostino Mattei ◽  
Sebastian Warncke ◽  
Christian Baermann ◽  
Ernst Peter Ritter ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sentinel Node ◽  
Image Fusion ◽  
Sentinel Node Detection ◽  
Operative Planning
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