Advanced algorithmic approaches to medical image segmentation
Distributed Markovian segmentation: Application to MR brain scans
Pattern Recognition
Boundary-specific cost functions for quantitative airway analysis
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
An efficient unsupervised mixture model for image segmentation
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A robust soft decision mixture model for image segmentation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Bi-exponential magnetic resonance signal model for partial volume computation
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Statistical models of partial volume effect for systems with various types of noise or pixel value distributions are developed and probability density functions are derived. The models assume either Gaussian system sampling noise or intrinsic material variances with Gaussian or Poisson statistics. In particular, a material can be viewed as having a distinct value that has been corrupted by additive noise either before or after partial volume mixing, or the material could have nondistinct values with a Poisson distribution as might be the case in nuclear medicine images. General forms of the probability density functions are presented for the N material cases and particular forms for two- and three-material cases are derived. These models are incorporated into finite mixture densities in order to more accurately model the distribution of image pixel values. Examples are presented using simulated histograms to demonstrate the efficacy of the models for quantification. Modeling of partial volume effect is shown to be useful when one of the materials is present in images mainly as a pixel component