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Performance analysis of multimodal medical image fusion using AMT-DWT-based pre-processing and customized CNN for denoising.

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Title: Performance analysis of multimodal medical image fusion using AMT-DWT-based pre-processing and customized CNN for denoising.
Authors: Ghosh, Tanima1,2 (AUTHOR), N., Jayanthi1 (AUTHOR) njayanthi@dce.ac.in
Source: Multimedia Tools & Applications. Jun2025, Vol. 84 Issue 20, p22269-22301. 33p.
Subjects: Discrete wavelet transforms, Image intensifiers, Image processing, Image fusion, Artificial intelligence, Diagnostic imaging
Abstract: Multimodal medical image fusion involves integrating information from various modality source images to create a fused image, facilitating straightforward and reliable diagnosis. In this paper, we present a novel approach utilizing an adaptive multilevel thresholding (AMT)- discrete wavelet transform (DWT)-based pre-processing method, leveraging the strengths of multiple image enhancement techniques through averaging and fusion in the frequency domain via DWT, which enhances the quality of the source image. The proposed enhancement technique is compared with several conventional image enhancement methods for performance validation. Thereafter, the enhanced images from different modalities are fused using an appropriate fusion rule such as conventional PCA or the Mean-Max fusion rule. To further de-noise and improve the visual quality of the fused image, a custom CNN incorporating two proposed activation functions is employed. This study aims to assess the performance of the mentioned fusion methods with the proposed activation functions. The proposed framework exhibits superior performance in terms of fusion metrics compared to many state-of-the-art image fusion models. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multimodal medical image fusion involves integrating information from various modality source images to create a fused image, facilitating straightforward and reliable diagnosis. In this paper, we present a novel approach utilizing an adaptive multilevel thresholding (AMT)- discrete wavelet transform (DWT)-based pre-processing method, leveraging the strengths of multiple image enhancement techniques through averaging and fusion in the frequency domain via DWT, which enhances the quality of the source image. The proposed enhancement technique is compared with several conventional image enhancement methods for performance validation. Thereafter, the enhanced images from different modalities are fused using an appropriate fusion rule such as conventional PCA or the Mean-Max fusion rule. To further de-noise and improve the visual quality of the fused image, a custom CNN incorporating two proposed activation functions is employed. This study aims to assess the performance of the mentioned fusion methods with the proposed activation functions. The proposed framework exhibits superior performance in terms of fusion metrics compared to many state-of-the-art image fusion models. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Image intensifiers
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      – SubjectFull: Image processing
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              Text: Jun2025
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