Abstract
Precise segmentation and analysis of underlying structures in an image is crucial for a variety of image analysis and computer vision applications including robot vision, pattern recognition and biomedical image processing such as image guided surgery, registration of multimodality images, cardiac motion tracking, etc. However, a robust identification and measurement of such structure is not always achievable by using a single technique that depends on a single image feature. Thus, it is necessary to make use of various image features, such as gradients, curvatures, homogeneity of intensity values, textures, etc. as well as model-based information (such as shape). Integration provides a way to make use of the rich information provided by the various information sources, whereby consistent information from the different sources are reinforced while noise and errors are attenuated. Integration is achieved in this work by using region information in addition to gradient information within the deformable boundary finding framework. The integration problem is framed in a Bayesian framework using shape and region priors to influence a gradient-based deformable boundary finding.
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Chakraborty, A., Duncan, J.S. (1998). Integrated Deformable Boundary Finding Using Bayesian Strategies. In: Erickson, G.J., Rychert, J.T., Smith, C.R. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 98. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5028-6_14
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DOI: https://doi.org/10.1007/978-94-011-5028-6_14
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