Abstract The importance of image encryption has considerably increased, especially after the dramatic evolution of the internet and network communications, due to the simplicity of capturing and transferring digital images. Although there are several encryption approaches, chaos-based image encryption is considered the most appropriate approach for image applications because of its sensitivity to initial conditions and control parameters. Confusion and diffusion methods have been used in conventional image encryption methods, but the ideal encrypted image has not yet been achieved. This research aims to generate an encrypted image free of statistical information to make cryptanalysis infeasible. Additionally, the motivation behind this work lies in addressing the shortcomings of conventional image encryption methods, which have not yet achieved the ideal encrypted image. The proposed framework aims to overcome these challenges by introducing a new method, Equal Pixel Values Quantization (EPVQ), along with enhancing the confusion and diffusion processes using chaotic maps and additive white Gaussian noise. Key security, statistical properties of encrypted images, and withstanding differential attacks are the most important issues in the field of image encryption. Therefore, a new method, Equal Pixel Values Quantization (EPVQ), was introduced in this study in addition to the proposed confusion and diffusion methods to achieve an ideal image encryption framework. Generally, the confusion method uses Sensitive Logistic Map (SLM), Henon Map, and additive white Gaussian noise to generate random numbers for use in the pixel permutation method. However, the diffusion method uses the Extended Bernoulli Map (EBM), Tinkerbell, Burgers, and Ricker maps to generate the random matrix. Internal Interaction between Image Pixels (IIIP) was used to implement the XOR (Exclusive OR) operator between the random matrix and scrambled image. Basically, the EPVQ method was used to idealize the histogram and information entropy of the ciphered image. The correlation between adjacent pixels was minimized to have a very small value (×10 −3 ). Besides, the key space was extended to be very large (2 450 ) considering the key sensitivity to hinder brute force attacks. Finally, a histogram was idealized to be perfectly equal in all occurrences, and the resulting information entropy was equal to the ideal value (8), which means that the resulting encrypted image is free of statistical properties in terms of the histogram and information entropy. Based on the findings, the high randomness of the generated random sequences of the proposed confusion and diffusion methods is capable of producing a robust image encryption framework against all types of cryptanalysis attacks.

Abstract This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.

Abstract Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. The image enhancement, Composite Median-Wiener (CMW) filter, improves image quality and preserves the edges. Moreover, the feature extraction method, Hierarchical Centroid Feature Method (HCM), is fused with the statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the existing methods. The results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification.

Abstract Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement.

Abstract Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

One of the major challenges and concerns of researchers is an early detection and diagnosis of thin-cap-fibro-atheroma or vulnerable plaque to prevent the sudden heart events. Recently, Virtual Histology (VH) as a new approach based on spectral analysis of Intravascular Ultrasound (IVUS) provides color coded of coronary tissue maps. In IVUS-VH image, plaque's components can be discriminated based on echogenicity. Nonetheless; available methods do not provide clinical relevant information about the pattern of plaque structure, plaque composition, and geometric position of each components, location or distribution of plaque components toward the lumen border. In this paper, we proposed a new method of feature extraction that plays a decisive role in vulnerable plaque detection including NCCL (Necrotic Core in Contact with the Lumen), DCCL (Dense Calcium in Contact with the Lumen), Confluent NC and Confluent DC.