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.