9478325
A Local Probabilistic Prior-Based Active Contour Model for Brain MR Image Segmentation
This paper proposes a probabilistic prior-based active contour model for segmenting human brain MR images. Our model is formulated with the maximum a posterior (MAP) principle and implemented under the level set framework. Probabilistic atlas for the structure of interest, e.g., cortical gray matter or caudate nucleus, can be seamlessly integrate into the level set evolution procedure to provide crucial guidance in accurately capturing the target. Unlike other region-based active contour models, our solution uses locally varying Gaussians to account for intensity inhomogeneity and local variations existing in many MR images are better handled. Experiments conducted on whole brain as well as caudate segmentation demonstrate the improvement made by our model.
31156941
A Robust Image Segmentation Model Based on Integrated Square Estimation
This paper presents a robust segmentation method based on the integrated squared error or L2 estimation (L2E). Formulated under the Finite Gaussian Mixture (FGM) framework, the new model (FGML2E) has a strong discriminative ability in capturing the major parts of intensity distribution without being affected by outlier structures or heavy noise. Comparisons are made with two popular solutions, the EM and FCM algorithms, and the experimental results clearly show the improvement made by our model.
23861231
The Manufacturing Advisory Service: Web-based process and material selection
The Manufacturing Advisory Service (MAS) is a concept level manufacturing process and material selection tool, geared toward educating designers in basic process capabilities and informing experienced designers about new technologies. The designer enters a dialogue with the MAS, answering questions about production quantity, typical tolerances, physical size, overall shape and cost requirements. At each step along the way, the user is presented with an updated, ranked list of manufacturing options. The list updates as soon as values are entered, and the underlying criteria that contribute to the rank are not obscured. This list can then be combined with material selection, resulting in an optimized matching of process/material pairs to the total set of requirements. The MAS is interlinked with web pages of instructions, tutorials, and references explaining the program, fabrication processes and material choices. The MAS performs its calculations using capability data from a remote database. The current version of the Manufacturing Advisory Service is located at: Http://cybercut.berkeley.edu/mas2/