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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Bakar, Suraya Abu
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Computer Science & IT

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A set of embedding rules in IWT for watermark embedding in image watermarking Hafidz, Muhammad Afnan; Ernawan, Ferda; Bakar, Suraya Abu; Fakhreldin, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1512-1520

Abstract

The development of new technologies has made image watermarking crucial in the digital era to preserve and protect illegal distribution of images against unauthorized users. This paper presents a robust image watermarking technique that employs a set of embedding rules in the three-level of integer wavelet transform (IWT). The proposed method aims to achieve high robustness of image watermarking while maintaining the imperceptibility. The proposed scheme divides the red and green layers into non-overlapping 16×16 blocks. Three levels of IWT are applied to obtain 2×2 LL sub-band, four coefficients of IWT are then modified based on the proposed set of rules for embedding watermark. The experimental results demonstrate a comparison of the proposed embedding and the existing methods. The proposed scheme produced an average NC value of 0.965 against the median filter. The results also showed the imperceptibility of the the image with a PSNR of 45.1760 db and SSIM of 0.9995.
A Comparative Study of Image Retrieval Algorithm in Medical Imaging Abdullah, Yang Muhammad Putra; Bakar, Suraya Abu; Hj Wan Yussof, Wan Nural Jawahir; Hamzah, Raseeda; Hamid, Rahayu A; Satria, Deni
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3447

Abstract

In recent times, digital environments have become more complex, and the need for secure, efficient, and reliable identification systems is growing in demand. Consequently, image retrieval has emerged as a critical area focusing on artificial intelligence and machine learning applications. Medical image retrieval has become increasingly crucial in today's healthcare field, as it involves accurate diagnostics, treatment planning, and advanced medical research. As the quantity of medical imaging data grows rapidly, the ability to efficiently and accurately retrieve relevant images from extensive datasets becomes critical. Advanced retrieval systems, such as content-based image retrieval, are imperative for managing complex data, ensuring that healthcare professionals can access the most relevant information to improve patient outcomes and advance medical knowledge. This paper compares three algorithms: Scale Invariant Feature Transform, Speeded Robust Features, and Convolutional Neural Networks in the context of two medical image datasets, ImageCLEF and Unifesp. The findings highlight the trade-offs between precision and recall for each algorithm, providing invaluable insights into selecting the most suitable algorithm for specific tasks. The study evaluates the algorithms based on precision and recall, two critical performance metrics in image retrieval.
Co-Authors Abdullah, Yang Muhammad Putra Deni Satria Fakhreldin, Mohammad Ferda Ernawan Hafidz, Muhammad Afnan Hamid, Rahayu A Hamzah, Raseeda Hj Wan Yussof, Wan Nural Jawahir