Neural network image fusion with PCA preprocessing

Depoian, Arthur C.; Jaques, Lorenzo E.; Xie, Da; Bailey, Colleen P.; Guturu, Parthasarathy

doi:10.1117/12.2588039

Cited by 2 publications

(2 citation statements)

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“…In order to determine whether the approach performs better for the type of imagery, several evaluation performance matrices were used for this set of images. Depoian et al [28] proposed a unique approach to obtaining better image fusion by combining PCA with a neural network. In comparison to conventional weighted fusion approaches, the employment of an auto-encoder neural network that is used to combine this information leads to better degree results in data visualization.…”

Section: Spatial Domain Fusion Techniquesmentioning

confidence: 99%

A Brief Analysis of Multimodal Medical Image Fusion Techniques

et al. 2022

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Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.

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Section: Spatial Domain Fusion Techniquesmentioning

confidence: 99%

A Brief Analysis of Multimodal Medical Image Fusion Techniques

et al. 2022

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“…Visual analysis and acceptable quality standards are employed together to analyse the merged data. The results are better than those of prior hybrid-fusion algorithms utilised to combine SAR and multispectral imagery Utilising PCA in combination with a neural network, Arthur C et al [14] PCA was used to suggest a novel method for increasing picture fusion quality. By using an auto encoder neural network instead of weighted fusion approaches, this information may be fused at a higher level of visualisation.…”

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confidence: 99%

A Novel MRI And CT Image Fusion Based on Discrete Wavelet Transform and Principal Component Analysis for Enhanced Clinical Diagnosis

Richa¹,

Kaur²,

Singh³

2022

Int. J. Onl. Eng.

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A large percentage of healthcare resources, including imaging tools, like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been dedicated to the management of affected patients in this pandemic of Coronavirus disease 2019 (COVID-19). The diagnostic modalities in medical research are improving at a rapid pace with an objective to acquire maximum information with as little data as possible without any artifacts. That is where image fusion comes into the picture. It is a technique of merging source medical pictures to maximize the necessary information. CT is generally used for bony structures, whereas MRI is more appropriate for soft tissues. A fusion of MRI and CT images would lead to enhancement of the overall image quality while giving comprehensive information, at the same time artifacts are also eliminated. Image fusion methods are applied in medical science and various other sectors. Several image processing techniques are used in medical diagnostics, like Principal Component analysis (PCA), Intensity-Hue-Saturation, Discrete Wavelet Transform (DWT), and others. This study suggests an image fusion algorithm utilising the principal component averaging and the DWT along with the performance analysis of the fusion of the MRI and CT images of brain. The technique used in our study significantly enhances the image quality in terms of various fusion performance measures that helps the medical practitioners to diagnose any infection and aids in its treatment.

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Neural network image fusion with PCA preprocessing

Cited by 2 publications

References 0 publications

A Brief Analysis of Multimodal Medical Image Fusion Techniques

A Brief Analysis of Multimodal Medical Image Fusion Techniques

A Novel MRI And CT Image Fusion Based on Discrete Wavelet Transform and Principal Component Analysis for Enhanced Clinical Diagnosis

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