The existing traditional cryptosystems, such as RSA, DES, IDEA, SAFER and FEAL, are not ideal for image encryption because of their slow speed and ineffectiveness in removing the correlations of the adjacent pixels. Meanwhile chaos-based cryptosystems, which have been extensively used over the past two decades, are almost all based on symmetric cryptography. Symmetric cryptography is much faster than asymmetric ciphers, but the requirements for key exchange make them hard to use. To remedy this imperfection, a hybrid-key based image encryption and authentication scheme is proposed in this paper. In particular, ergodic matrices are utilized not only as public keys throughout the encryption/decryption process, but also as essential parameters in the confusion and diffusion stages. The experimental results, statistical analysis and sensitivity-based tests confirm that, compared to the existing chaos-based cryptosystems, the proposed image encryption scheme provides a more secure means of image encryption and transmission.

The registration of CT and MR images is important to analyze the effect of PCL and ACL deficiency on knee joint. Because CT and MR images have different limitations, we need register CT and MR images of knee joint and then build a model to do an analysis of the stress distribution on knee joint. In our project, we adopt image registration based on mutual information. In the knee joint images, the information about adipose, muscle and other soft tissue affects the registration accuracy. To eliminate the interference, we propose a combined preprocessing solution BEBDO, which consists of five steps, image blurring, image enhancement, image blurring, image edge detection and image outline preprocessing. We also designed the algorithm of image outline preprocessing. At the end of the paper, an experiment is done to compare the image registration results without the preprocessing and with the preprocessing. The results prove that the preprocessing can improve the image registration accuracy.

Lung cancer has become one of the leading causes of death in the world. Clear evidence shows that early discovery, early diagnosis and early treatment of lung cancer can significantly increase the chance of survival for patients. Lung Computer-Aided Diagnosis (CAD) is a potential method to accomplish a range of quantitative tasks such as early cancer and disease detection. Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3Dmethods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand. Finally, all malignant nodules were detected and a very low false-positive detection rate was achieved. The automated extraction of the lung in CT images is the most crucial step in a computer-aided diagnosis (CAD) system. In this paper we describe a method, consisting of appropriate techniques, for the automated identification of the pulmonary volume. The performance is evaluated as a fully automated computerized method for the detection of lung nodules in computed tomography (CT) scans in the identification of lung cancers that may be missed during visual interpretation.

The aim of this study is to improve the visual quality of x-ray CR images displayed at general displays. Firstly, we investigate a series of wavelet-based denoising methods for removing quantum noise remains in the original images. The denoised image is obtained by using the scheme of wavelet thresholding, where the best suitable wavelet and level are chosen based on theory analysis. Secondly, the image contrast is enhanced using Gamma correction. Thirdly, we improve unsharp masking method for enhancing some useful information and restraining other information selectively. Fourthly, we fuse the denoised image with the enhanced image. Fively, the used display is calibrated, so that it could offer full compliance with the Grayscale Standard Display Function (GSDF) defined in Digital Imaging and Communications in Medicine (DICOM) Part 14. Finally, we decide parameters of the image fusion, resulting in the diagnosis image. A number of experiments are performed over some x-ray CR images by using the proposed method. Experimental results show that this method can effectively reduce the quantum noise while enhancing the subtle details; the visual quality of X-ray CR images is highly improved.

The hippocampal CA1 pyramid neuron has plenty of discharge actions. The one-compartment model of CA1 pyramid neuron developed by David is a nine-dimension complex dynamic model. In the thesis, the currents related to the nine-dimension complex model are analyzed and classified by the model’s reduction theory and methods based on neurodynamics, and four minimal models are gotten: (I_Na+I_Kdr)-minimal model, (I_Na+I_M)-minimal model, (I_Na+I_Ca+I_y)-minimal model, and (I_Na+I_Ca+I_sAHP)-minimal model. These minimal models have plenty of dynamic actions, and under the current’s stimulation, they can all generate regular discharge and have period discharge pattern, bursting pattern, the chaos discharge pattern, and so on. Compared with the initial nine-dimension complex model, these minimal models’ dimension are much reduced, and are more convenient to numerical simulation, calculating, and analyzing. In addition, these minimal models provide a simpler and flexible method to discuss the specific currents’ dynamic characteristics and functions of the initial nine-dimension complex model by the theory of neurodynamics.

In this paper, the lattice-Boltzmann method is developed to investigate the behavior of isothermal two-phase fluid flow in porous media. The method is based on the Shan–Chen multiphase model of nonideal fluids that allow coexistence of two phases of a single substance. We reproduce some different idealized situations (phase separation, surface tension, contact angle, pipe flow, and fluid droplet motion, et al) in which the results are already known from theory or laboratory measurements and show the validity of the implementation for the physical two-phase flow in porous media. Application of the method to fluid intrusion in porous media is discussed and shows the effect of wettability on the fluid flow. The capability of reproducing critical flooding phenomena under strong wettability conditions is also proved.

Based on the irradiance calculation of all pixels on the focal plane array, a preliminary infrared imaging prediction model of exhaust plume that have considered the geometrical and the thermal resolution of the camera was developed to understanding the infrared characteristics of exhaust plume. In order to compute the irradiance incident on each pixel, the gas radiation transfer path in the plume for the instantaneous field of view corresponds to the pixel was solved by the simultaneous equation of a enclosure cylinder which covers the exhaust plume and the line of sight. Radiance of the transfer path was calculated by radiation transfer equation for nonscattering gas. The radiative properties of combustion needed in the equation was provided by employing Malkmus model with EM2C narrow band database(25cm-1). The pressure, species concentration along the path was determination by CFD analysis. The relative irradiance intensity of each pixel was converted to color in the display according to gray map coding and hot map coding. Infrared image of the exhaust plumes from a subsonic axisymmetric nozzle with different relative position of camera and the plume was predicted with the model. By changing the parameters, such as FOV and space resolution, the image of different imaging system can be predicted.

To address the spatial Morphological analysis of complex geological bodies in stereoscopic quantitative prediction of concealed ore bodies, a three-dimensional morphological analysis method for geological bodies based on 3-dimensional raster model under visualization environment was put forward by combining mathematical morphology with Euclidean distance transform theory. Firstly, the 3-dimensional visualization models for geological bodies were constructed on the basis of the 3-dimensional geological modeling (3DGM) technology; Secondly, the algorithm for extracting the surface shape trend of geological body with the 3-dimensional raster model was proposed by using mathematical morphology filtering. By the combination of morphological filtering, global set operation and three-dimensional Euclidean distance transform, the models for the quantitative analysis and hierarchical extraction of the shape undulance were established. Lastly, as a case study, the three-dimensional morphological analysis method was applied in analyzing quantitatively the Xinwuli magmatic body in Fenghuangshan ore field in Tongling, Anhui Province. By means of the calculation model of Euclidean distance field, the quantitative extraction of the shape trend and shape undulance as well as the angle between geological interface and trend surface, as the quantitative indexes of geological ore-controlling factors, were achieved after building the 3D raster models of the magmatic body. The results show that the morphological analysis method is feasible to calculate various morphological parameters of complex geological bodies and extract quantitative indexes of geological ore-controlling factors successfully for stereoscopic quantitative predication of concealed ore bodies.