Value of a probabilistic atlas in medical image segmentation regarding non-rigid registration of abdominal CT scans
ABSTRACT A probabilistic atlas provides important information to help segmentation and registration applications in medical image analysis. We construct a probabilistic atlas by picking a target geometry and mapping other training scans onto that target and then summing the results into one probabilistic atlas. By choosing an atlas space close to the desired target, we construct an atlas that represents the population well. Image registration used to map one image geometry onto another is a primary task in atlas building. One of the main parameters of registration is the choice of degrees of freedom (DOFs) of the geometric transform. Herein, we measure the effect of the registration’s DOFs on the segmentation performance of the resulting probabilistic atlas. Twenty-three normal abdominal CT scans were used, and four organs (liver, spinal cord, left and right kidneys) were segmented for each scan. A well-known manifold learning method, ISOMAP, was used to find the best target space to build an atlas. In summary, segmentation performance was high for high DOF registrations regardless of the chosen target space, while segmentation performance was lowered for low DOF registrations if a target space was far from the best target space. At the 0.05 level of statistical significance, there were no significant differences at high DOF registrations while there were significant differences at low DOF registrations when choosing different targets.
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ABSTRACT: Wood has the texture of natural beauty and elegant color so that it can be widely used in the construction and furniture. So people put the special focus on the texture of wood and they are nearly particular about its color. According to the conventional segmentation method, the characteristics of the wood and the actual production process in a short time of the request, this paper presents a segmentation method of wood panel based on color difference and mathematical morphology. In the HSI space, it firstly focus on select morphological edge detection of H component and I component. Instead of considering the small pixel blocks, it uses median filter to clear it to retain accurate edge image. So edge detection is characterized by the H-component of the color model, then region growing is based on edge information, in order to overcome defects of pseudo edge. In this paper, it uses the boundary information to select seed points automatically, then it takes a regional model of the regional boundary for the region growing, and finally it splits out a defective portion of the timber well.Optik - International Journal for Light and Electron Optics 02/2014; 125(3):965–967. DOI:10.1016/j.ijleo.2013.07.098 · 0.77 Impact Factor