Global And Local Adaption Of Geometric Deformable Models For Medical Image Segmentation PDF Download

Medical image analysis and visualization has become increasingly important in computer aided medicine. Throughout the history of medicine, advances in imaging have led to great progress in medical interventions. The thesis proposes, develops and evaluates methods for automated analysis, visualization and quantification of medical images. The focus of this thesis is to perform both theoretical and practical investigations into medical image analysis and visualization to overcome current challenges in the field. The theoretical framework for fulfilling above goals is based on segmentation using the geometric deformable model and some new advances: support vector machine and principal component analysis from the pattern recognition and machine learning. The medical applications of the above theoretical framework include automated computer aided analysis of dental X-ray image and chest computer tomography volumetric image reconstruction and visualization. There are three main contributions in the thesis: (1) We propose and develop two faster and more robust segmentation methods which have the potential to be used in clinical and hospital environments. (2) We propose and develop the first dental X-ray image analysis and visualization system. It is able to analyze the dental X-ray image, extract the features and then recognize the patterns of certain diseases such as root decay and areas of bone loss. It has potential to be applied in the dental X-ray machine which has attracted interest from industry. (3) We propose and develop an efficient reconstruction and visualization framework. This method can reconstruct and visualize very large medical datasets with less time and less data volume.