Medical image fusion with Laplacian Pyramids

Sahu, Akanksha; Bhateja, Vikrant; Krishn, Abhinav; Himanshi,

doi:10.1109/medcom.2014.7006050

Cited by 62 publications

(24 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pyramidal decomposition [250,[264][265][266][267][268][269][270][271][272][273][274] and multi-resolution [275,276] techniques have solved this problem, citing fusion by the gradient pyramid, the Laplacian pyramid, the contrast pyramid, etc. On the other hand, the output image suffers from a reduced contrast with pyramidal fusion and slightly less with multi-resolution techniques.…”

Section: Critical Discussion About Multimodal Fusion Techniquesmentioning

confidence: 99%

“…An image pyramid contains all the information necessary to reconstruct the original image. Among the techniques based on pyramidal decomposition, we could cite the Laplacian technique [250,[264][265][266][267][268][269][270], Gaussian technique [269] gradient pyramid [270], low-pass ratio pyramid [271], contrast [272] and the morphological technique [273]. Pyramid techniques provide good visual quality for a multi-focus image.…”

Section: Pyramid Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

View full text Add to dashboard Cite

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

show abstract

Section: Critical Discussion About Multimodal Fusion Techniquesmentioning

confidence: 99%

Section: Pyramid Decompositionmentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

View full text Add to dashboard Cite

show abstract

“…Based on this theory, multiple pyramid fusion algorithms (e.g., Gaussian pyramid and Laplacian Pyramid) have been proposed with different pyramid decomposition structures, fusion rules and reconstruction methods. In the medical field, the pyramid method has been applied in fusing multimodal medical images, such as MRI/CT, PET/MRI and SPECT/MRI [57][58][59].…”

Section: Pyramid Methodsmentioning

confidence: 99%

“…For the fusion of CT and MRI in real clinical applications, the common methods are Arithmetic combination [177], the PCA-wavelet transformation-based method [47][48][49]53,54,56,117] and the Pyramid method [58,59]. Here, we show one real clinical example for the assessment of the cryoablation margin using MRI-CT fusion imaging in hepatic malignancies [180].…”

Section: Surgical Operation Assistancementioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

“…In previous research, pyramid methods and WT are most commonly used, such as Laplacian pyramid 16,17 and discrete wavelet transform (DWT). [18][19][20] In addition to these, some multiscale transform methods are proposed, such as dualtree discrete WT (DT-DWT), 21 curvelet transform, 22 contourlet transform, 23 and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on fusion technology based on low-light visible image and infrared image

2016

View full text Add to dashboard Cite

Abstract. Image fusion technology usually combines information from multiple images of the same scene into a single image so that the fused image is often more informative than any source image. Considering the characteristics of low-light visible images, this study presents an image fusion technology to improve contrast of lowlight images. This study proposes an adaptive threshold-based fusion rule. Threshold is related to the brightness distribution of original images. Then, the fusion of low-frequency coefficients is determined by threshold. Pulsecoupled neural networks (PCNN)-based fusion rule is proposed for fusion of high-frequency coefficients. Firing times of PCNN reflect the amount of detail information. Thus, a high-frequency coefficient corresponding to maximum firing times is chosen as the fused coefficient. Experimental results demonstrate that the proposed method obtains high-contrast images and outperforms traditional fusion approaches on image quality. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Medical image fusion with Laplacian Pyramids

Cited by 62 publications

References 47 publications

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Research on fusion technology based on low-light visible image and infrared image

Contact Info

Product

Resources

About