Segmentation of Magnetic Resonance Microimages of Trabecular Bone: Classifiers and Markov Random Field Model
نویسندگان
چکیده
Quantitative assessment of trabecular bone structure based on magnetic resonance microimages requires a segmentation step, which is difficult to perform because of low signal-to-noise ratio and spatial signal inhomogeneities in these images. In this paper, we present the design of voxel classifiers based on statistical mixture models and classifiers using the feed-forward artificial neural networks (ANN). In both cases a Markov random field (MRF) prior model is used to enhance the reliability of the segmentation process.
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