Studying On The Key Techniques Of Volume Visualization In Medicine

Volume visualization in medicine is an advanced topic in the field of Scientific Visualization and the Biomedical Information Engineering. This technology has rapidly developed greatly in recent years, and it has widely been applied to the clinical diagnosis. Accurate extraction of the tissues or organs, real-time interactive reaction of the manipulation and high quality of the rendering image are the key techologys of the whole visualization of the medical volume data field. Accurate extraction of the tissues or organs is the important basis of the study of anatomical structure, quantification of tissue volumes, location of pathology, diagnosis, treatment planning, and computer aided surgery, etc; Real-time interactive reaction of the manipulation is the requirement of the further extend application of medical visualization in the clinical diagnosis and treatment; Due to the increasing amount of medical image data, it is necessary to visualize the date with high quality image rendering.The thesis deeply studies the key techniques mentioned above, including obtaining and preprocessing of the medical data, interpolation between slices, contrast enhancement based on the fuzzy sets and rough sets theories for the medical field, extraction of the tissues or organs of interest based on the volume rendering and the rendering techniques for the medical volume data. In addition, some research on the application and realization of these technologies in the medical assistant diagnosis and treatment are also involved in this dissertation.The main contents and contributions of this dissertation are as follows:(1) The Cubic convolution interpolation is deeply investigated. The interpolation principle of Cubic convolution is formulated by the numbers, including the differences and relations among the Cubic interpolation methods with the dissimilar sharp control parameters. Based on these, an interpolation method for the medical volume data field based on the optimal Cubic kemel is presented. The method makes full use of the local characteristic of the original cross-sections, and then the optimal parameter is determined by iterative operation. Finally, the volume data field is interpolated by the Cubic convolution with the optimal parameter in one operation, which avoids the error transfer of the conventional methods. So the interpolationprecision is improved effectively.(2) A method of the interpolation between slices based on the pixel classification is presented. The method classes the pixels into two groups, and then the pixels of the different groups are evaluated by the different ways, which can effectively avoid the loss of the edge information. This method is especially effective for the volume data with large distance between slices. In addition, a method for interpolation of the volume data field is presented; it is based on edge information. Firstly, the boundary of the interpolated image is determined using weighted mean of the profile, and then the value of the boundary pixels is evaluated by the best matching corresponding points. The experimental results show that, the approach not only improves the interpolation precision as well as efficiency.(3) A fast algorithm is improved for the fuzzy contrast enhancement, and it is based on the dual-linear transform. Due to the linear subjectional function and the linear fuzzy enhancement operator, the algorithm not only speed the process, but also voids the losses of particular information. Based on these, an algorithm combining the rough sets is presented for the contrast enhancement of volume data field. In this algorithm, the volume data is classified using the rough sets theory, and the noise is removed simultaneously, then the classified sub-volume data is enhanced in different ways. The algorithm can effectively avoid the cover of background when the interested tissues and organs are displayed.(4) A windows function is improved. Based on the function, an approach based on volume rendering is developed for extracting the VOI (Volume of Interest) from a medical dataset. The most tissues and organs of human can be extracted by adjusting the parameters.(5) A volume rendering algorithm which suits the medical volume data field is presented. The algorithm not only solves the problem of image blur of the classical Ray Casting algorithm, but also the displayed image accords with the vision characteristic of human perfectly.Taking advantage of the above-mentioned technologies, an experimental system named VolMTDSys is designed and realized to assist diagnosis and assist doctors. The system is that kind of interactive assistant diagnosis system which has a new full filed vision and full space vision navigation. In comparison with traditional image aided diagnosing system, the new system has the comparative advantages of high real-time, strong feeling of immersion and good visibility.