A Survey on Image Fusion Requirements, Techniques, Evaluation Metrics, and Its Applications

Narsaiah, M.N.; Vathsal, S.; Reddy, D. V.

doi:10.14419/ijet.v7i2.20.14774

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Three levels of operations are performed in image fusion techniques [15,16]. In the pixel level, the images of individual pixels are directly combined.…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

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A Medical imaging plays a vital role in medical diagnosis and clinical treatment. The biggest challenge in the medical field is the correct identification of disease and better treatment. Multi-modal Medical Image Fusion (MMIF) is the process of merging multiple medical images from different modalities into a single fused image. The main objective of the medical image fusion is to obtain a large amount of appropriate information (i.e., features) to improve the quality and makes it more informative for increasing clinical therapy for better diagnosis and clear assessment of medical related problems. The MMIF is generally considered with MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), MRA (Magnetic Resonance Angiography), T1-weighted MR, T2-weighted MR, X-ray, and ultrasound imaging (Vibro-Acoustography).This review article presents a comprehensive survey of existing medical image fusion methods and have been characterized as six parts: (1) Multi-modality medical images, (2) literature review process, (3) image fusion rules, (4) quality evaluation metrics for assessment of fused image, (5) experimental results on registered datasets and (6) Conclusion. In addition, this review article provides scientific challenges faced in MMIF and future directions for better diagnosis. It is expected that this review will be useful to establish a concrete foundation for developing more valuable fusion methods for medical diagnosis.

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“…Three levels of operations are performed in image fusion techniques [15,16]. In the pixel level, the images of individual pixels are directly combined.…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

View full text Add to dashboard Cite

show abstract

“…All MMIF are categorized into three levels: decision level, pixel level, and feature level [ 5 , 6 ]. However, structural and spectral enhancement deals with pixel information.…”

Section: Introductionmentioning

confidence: 99%

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

Khan,

Ullah

et al. 2023

Heliyon

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Medical Image Fusion for Multiple Diseases Features Enhancement

Khan,

Alharbi,

Shah

et al. 2024

Int J Imaging Syst Tech

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Throughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

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A Survey on Image Fusion Requirements, Techniques, Evaluation Metrics, and Its Applications

Cited by 4 publications

References 12 publications

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

Medical Image Fusion for Multiple Diseases Features Enhancement

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