This study presents a novel encryption method for the security of DICOM (Digital Imaging and Communications in Medicine) images used in medical applications. The proposed algorithm splits the original image into blocks of size 16 × 16 pixels, then encrypts it through three steps. Firstly, the keys k ₁ , k ₂ , k ₃ and k ₄ are transformed from four vectors of 16 pixels to a matrix of 16 × 16 pixels. Then, the proposed algorithm encrypts the image block‐by‐block using the Vigenère cipher algorithm. For each block, the proposed algorithm modifies the key using Arnold transform. The proposed encryption algorithm is scalable with colour images and JPEG (Joint Photographic Experts Group) compression. The cryptanalysis of the proposed algorithm demonstrates that it passed the cryptography attacks tests with success. Its running time shows that it is faster than a typical and recent image encryption algorithm.

In this study, the authors propose a hardware implementation of a local cloud for storing, sharing and archiving patient's folders (health report and medical imaging) based on raspberry Pi card and a local cloud (hard disc) while exchanging with other distant public cloud (google drive, azure cloud, …). Securing the medical data is ensured by a proposed encrypting method for the watermarked digital imaging and communications in medicine imaging which contains the patient information (the watermark) and the advanced encryption standard is used to crypt the text files (health reports). The imaging encryption method is based on the exoring of the i th block to be encrypted and the i th block key. The watermarking method is based on the insertion of the watermark in the least significant bit. Finally, encryption and watermarking keys are encrypted and stored in the database. A real‐time implementation on raspberry Pi and android smartphone is proposed for the whole system using, respectively, PHP and android studio. Experimental results demonstrate that the proposed security method is robust against different types of attacks and can achieve high security with a good performance.

In this study, the authors present a secured transfer method for medical images, using smartphone, based on a proposed images encryption algorithm using the matrix product and the exclusive addition. The novelty of this study is to propose a low‐complexity encryption algorithm running in real time on embedded system. Experimental results demonstrate that the proposed encryption method can achieve high security with a good performance.

Detecting moving objects in videos is an evolving area of research, with important implications in many computer vision applications. In this paper, we propose a new detection approach by combining background subtraction and multi-level image thresholding based on fuzzy entropy, powered by the differential evolution (DE) algorithm. The first step of our method is background subtraction, aiming to isolate moving objects by eliminating the static background. However, this approach can be sensitive to lighting variations and background changes, thus limiting its accuracy. To overcome these limitations, we introduce multi-level image thresholding based on fuzzy entropy. This method exploits the intrinsic variability of moving objects rather than simply differentiating against the background. By adjusting thresholds locally, our approach better adapts to changing environmental conditions. The key element of our proposal lies in the optimization of the fuzzy entropy threshold parameters using the differential evolution algorithm. We chose DE for its robustness and efficiency in handling continuous optimization problems, which makes it well-suited for complex tasks like multi-level image thresholding. By iteratively adjusting the thresholds, we maximize the detection of moving objects while minimizing false positives, thereby improving the robustness and accuracy of the method. Our experiments on test video sequences demonstrate the effectiveness of our approach, highlighting a significant improvement in moving object detection compared to traditional methods. This promising methodology paves the way for future advances in moving object detection, with potential applications in surveillance, robotics, and computer vision in general.