Visual netlogo-based simulation of anti-SARS immune system and low-to-high resolution reconstruction of sequence medical ct images anti-sars CT

In the immune responses against the SARS (Severe Acute Respiratory Syndromes), human immune systems are complex intelligent systems, which show good properties such as the self-organizing and adaptivity. Modeling the immune systems has important significance in both immunology and artificial immune system. In order to improve the visualization and readability of the anti-SARS immune system model, the visual tri-tier computational model of the anti-SARS immune system was simulated with NetLogo, which is a multi-agent-based tool. On the other hand, to fight against the SARS disease, the lowresolution medical CT (Computed Tomography) images should be transformed into the high-resolution ones for better SARS analysis. In order to obtain the high-resolution image from some low-resolution chest CT sequence images of a SARS patient, the low-to-high resolution reconstruction was designed and tested in this paper. First, the low-resolution medical images were preprocessed. Then the pretreated low-resolution medical images were registered with the sub-pixel-level image registration techniques. Finally, the POCS (Projections onto Convex Sets) image reconstruction algorithm was designed and tested. We obtained higher entropy and more detail information of the medical images with our approach than the Marcel method, especially for the rotated medical images in our experiments. Multiple-user browser-based experimental results show that the visual NetLogo-based simulation of the immune system is better to understand than the traditional mathematic equation model of the immune system.