Abstract The purpose of this research is to develop a new method for encrypting multiple superimposed or side-by-side images. The process begins by extracting the red, green, and blue channels from each image and converting them into vectors that combine to produce a single image that undergoes an advanced pixel-level Vigenere transform. In the next step, a pseudorandom transition occurs at the nucleotide, followed by a passage to codons for genetic crossover implementation specifically designed for image scrambling. The latter process is controlled by many random tables developed from selected chaotic maps, which ensures a high degree of flexibility and security in the encryption method. To evaluate the effectiveness and security of this innovative multi-image encryption algorithm, extensive simulations were performed using a large number of images randomly selected from the database. The simulation results prove the reliability and robustness of the method.

Abstract

Radiomics, a quantitative approach to medical imaging, employs computational methods to extract features from the images, revealing hidden characteristics of specific regions. This emerging field leverages advanced techniques to analyze a spectrum of features from modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans, aiming to decode tissue characteristics, disease progression, and treatment responses. The radiomics workflow integrates image acquisition, segmentation, feature selection, and data integration, utilizing advanced techniques such as deep learning, machine learning, and data mining. Radiomics demonstrates considerable potential in cancer detection and management, exhibiting high sensitivity and specificity in distinguishing between benign and malignant tumors and predicting outcomes. However, challenges such as imaging protocol variability, overfitting, and standardization requirements impede its broad clinical adoption. Innovations in multi-modal radiomics, deep learning, and genomics integration strive to mitigate these constraints. This review elucidates radiomics’ capabilities, current applications, benefits, challenges, and future directions in oncology.

Abstract

Advancements in Medical Image Segmentation have revolutionized clinical diagnostics and treatment planning. This review explores a wide range of segmentation techniques applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, emphasizing their clinical implications and future directions. CT segmentation techniques, including U-Net and its variant nnU-Net, are essential in oncology for precise tumor delineation, in cardiology for coronary artery analysis, and in pulmonology for lung lesion detection. These methods enhance radiotherapy targeting, surgical planning, and overall diagnostic accuracy. The nnU-Net, known for its self-configuring nature, is particularly notable for setting new benchmarks in medical image segmentation tasks. MRI segmentation benefits from superior soft tissue contrast. Techniques like Mask Region-based Convolutional Neural Network (R-CNN) excel in identifying brain lesions, assessing musculoskeletal injuries, and monitoring soft tissue tumors. These methods support detailed visualization of internal structures, improving diagnosis and guiding targeted interventions. U-Net architectures also play a critical role in MRI segmentation, demonstrating high efficacy in various applications such as brain and prostate imaging. A systematic review of the literature reveals performance metrics for various segmentation techniques, such as accuracy, sensitivity, specificity, and processing time. Traditional methods like thresholding and edge detection are contrasted with advanced deep learning and machine learning approaches, highlighting the strengths and limitations of each. The review also addresses methodological approaches, including data collection, analysis, and evaluation metrics. Future prospects include integrating 3D and 4D segmentation, multimodal data fusion, and enhancing AI explain ability. These innovations aim to refine diagnostic processes, personalize treatments, and improve patient outcomes. Clinical applications of these segmentation techniques demonstrate significant advantages in radiology, oncology, and cardiology, though challenges such as data variability and noise persist. Emerging strategies like data augmentation and transfer learning offer potential solutions to these issues. The continuous evolution of medical image segmentation techniques promises to enhance diagnostic accuracy, efficiency, and the personalization of patient care, ultimately leading to better healthcare outcomes.