Developing a Computer-Assisted Detection (CAD) system for automatic detection of pulmonary nodules in thoracic CT is a highly challenging research area in the medical domain. It requires the application of state-of-the-art image processing and pattern recognition technologies. The object recognition and feature extraction phase of such a system generates a large number of data set. As there is normally a large quantity of non-nodule objects within this data set while the nodule objects are sparse, a Gaussian mixture model-based sampling method is used to reduce the non-nodule data and thus the classification complexity. The support vector machine, a classifier motivated from the statistical learning theory, is used in the pattern recognition stage of automatic pulmonary nodule detection. After the training process, only support vectors will be used in the classification process. As the support vector machine classifier gives the unique optimal solution, the experiment on the lung nodule data shows a fast and satisfactory classification rate.

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An automated method to register Medical Images is presented in this work. This new technique employs an iterative w arping scheme and is performed, either over fully pixel information or using geometrical features such as ridges. The warp at each iteration is given by a n umberof local Finite Element transformations which p r o vide a non-linear global warp. Results show that this technique can be applied satisfactorily to images with a severe level of distortions.