<p> A new parameter-free texture feature-based seeded region growing algorithm is proposed in this dissertation for automated segmentation of organs in abdominal MR images. This algorithm requires that a user only mouse clicks twice to identify the upper left and lower right corners of a rectangular region of interest (ROI). With this given ROI, a seed point is automatically selected based on homogeneity criteria. Intensity as well as four texture features: 20 cooccurrence texture features, Gabor texture feature, and both 20 and 3D semivariogram texture features are extracted from the image and a seeded region growing algorithm is performed on these feature spaces. A threshold is then obtained by taking a lower value just before the one which results in an ' explosion '. An optional Snake post-processing tool is also provided to obtain better organ delineation. The comparative results of the texture features and intensity are reported using both normal digital images and abdominal MR images acquired from ten patients. Comparisons of Before and After Snake are also presented. Generally, Gabor texture feature is found to perform the best among all features . The experimental results of the proposed approach show that it is fast and accurate when combined with Gabor texture feature or intensity feature and should prove a boon to production radiological batch processing. </p> / Thesis / Doctor of Philosophy (PhD)