Forthcoming and Online First Articles

International Journal of Signal and Imaging Systems Engineering

International Journal of Signal and Imaging Systems Engineering (IJSISE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Signal and Imaging Systems Engineering (2 papers in press)

Regular Issues

  • MDLNet: Fusion of Magnetic Resonance and Nuclear Medicine Brain Images Using Deep Learning Based Techniques   Order a copy of this article
    by Syed Munawwar, P. V. Gopi Krishna Rao 
    Abstract: High-quality multi-modal picture fusion is critical in medical imaging to improve diagnosis accuracy. Medical multi-modal image fusion creates more comprehensive and accurate images by combining data from many imaging modalities. An effective deep learning-based model is proposed in this research for medical image fusion. To enhance the image quality, the MDLNet model is trained and optimised using multi-scale feature extraction network and multi-scale feature fusion network models. A modified ResNet-50 model is used to extract the local features, and the DenseNet-169 model is used to extract global features. The method known as the Artificial Gorilla Troops Optimiser (AGTO) is used to choose significant features. Furthermore, a novel multi-scale feature fusion network named the modified FasterNet model is used to fuse selected local and global features. The final fused images are created by reconstructing fused features using three 3
    Keywords: Image fusion; modified ResNet-50; DenseNet-169 model; modified FasterNet; and convolutional kernels.
    DOI: 10.1504/IJSISE.2025.10072031
     
  • Region-Based Medical Image Encryption for Patient Privacy   Order a copy of this article
    by Subhadip Rana, Arghya Pathak, Hrishikesh Mondal, Mrinal Mandal 
    Abstract: In this paper, a region-based medical image encryption using chaos is presented. The medical image is first segmented into two distinct parts: the Region of Interest (ROI), which contains nearly all significant information, and the non-ROI, which carries minimal information. The encryption process for the ROI demands high quality without any loss, in stark contrast to the non-ROI segment. To optimize the encryption process, reduce time, and minimize required memory during transmission, a JPEG 2000 lossy mechanism is employed to compress the non-ROI segment. Subsequently, the entire information from both the ROI and the compressed non-ROI is subjected to encryption using a 4D hyper-chaotic system. The proposed cryptosystem's robustness is evaluated through an analysis of various test parameters, such as information Entropy, PSNR, NPCR, UACI, histogram analysis, etc.
    Keywords: Medical Image Encryption; Region of Interest; JPEG 2000; 4-D Hyperchaotic System.
    DOI: 10.1504/IJSISE.2025.10072315
     

 

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