A Novel Technique for Fusing Multimodal and Multiresolution Brain Images

Chandrashekar, Leena; Sreedevi, A.

doi:10.1016/j.procs.2017.09.112

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The datasets chosen belong to Group I representing large tumors and Group II with small tumors. In 3 shows the expressions for various performance parameters, the detailed definitions are discussed in [30][31][32][33]. The proposed enhancement technique provides high PSNR, SSIM, UIQI and a minimum MSE.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

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Section: Experiments and Resultsmentioning

confidence: 99%

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

Chandrashekar¹,

Sreedevi²

2021

Emerging Research in Computing, Information, Communication and Applications

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“…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%

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

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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.

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Framework for Multimodal Image Fusion for Detection of Glioblastoma

Chandrashekar¹,

Sreedevi²

2020

2020 IEEE 17th India Council International Conference (INDICON)

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A Novel Technique for Fusing Multimodal and Multiresolution Brain Images

Cited by 4 publications

References 11 publications

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

A Nature Inspired Algorithm for Enhancement of Fused MRI and CT Brain Images

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

Framework for Multimodal Image Fusion for Detection of Glioblastoma

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