Neuro‐wavelet based intelligent medical image fusion

Hassan, Mehdi; Murtza, Iqbal; Khan, Muhammad Aqdus Zafar; Tahir, Syed Fahad; Fahad, Labiba Gillani

doi:10.1002/ima.22347

Cited by 4 publications

(3 citation statements)

References 69 publications

(100 reference statements)

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“…The author's contributed work represents a quality fused image with directionality, and better visualization, and is 35% superior to the state-of-the-art fusion methods such as NSCT, NSST, etc. Mehdi Hassan et al [87] suggested neuro-wavelet based MIF. The low and high frequency coefficients are fused by using the neural network with extracted features.…”

Section: Hybrid and Optimization Algorithms Based Medical Image Fusionmentioning

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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Section: Hybrid and Optimization Algorithms Based Medical Image Fusionmentioning

confidence: 99%

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

Haribabu

Guruviah

Yogarajah

2023

CMIR

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“…The image alignment is performed via rotating, shifting, and scaling. The performance instability to the changed data set and color distortion is considered as the important limitations during image fusion 11‐13 …”

Section: Introductionmentioning

confidence: 99%

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Sumithra

Malathi²

2020

Int J Imaging Syst Tech

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Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

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“…Seal and Panigrahy [18] focused on translation-invariant à trous wavelet transform and fractal dimension using a differential box counting method. Hassan et al [19] implemented image fusion methods that are combined with wavelet transform and the learning ability of artificial neural networks. In recent years, deep learning networks have also been used to execute image fusion [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Jian

Chu

et al. 2020

Applied Sciences

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This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

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Neuro‐wavelet based intelligent medical image fusion

Cited by 4 publications

References 69 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

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

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