Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer
Pattern Recognition
Automated 3D segmentation of lung fields in thin slice CT exploiting wavelet preprocessing
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Autism diagnostics by 3D texture analysis of cerebral white matter gyrifications
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Graph cuts framework for kidney segmentation with prior shape constraints
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Novel stochastic framework for accurate segmentation of prostate in dynamic contrast enhanced MRI
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
3D Graph cut with new edge weights for cerebral white matter segmentation
Pattern Recognition Letters
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities
Journal of Medical Systems
Appearance models for robust segmentation of pulmonary nodules in 3d LDCT chest images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A new CAD system for the evaluation of kidney diseases using DCE-MRI
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A novel approach for image alignment using a markov–gibbs appearance model
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A new adaptive probabilistic model of blood vessels for segmenting MRA images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A novel approach for global lung registration using 3d markov-gibbs appearance model
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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We propose new techniques for unsupervised segmentation of multimodal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels. We follow the most conventional approaches in that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. However, our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. We modify an expectation-maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation with which the modified EM algorithm should start. The proposed technique identifies individual LCG models in a mixed empirical distribution, including the number of positive and negative Gaussians. Initial segmentation based on the LCG models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments show that the developed techniques segment different types of complex multimodal medical images more accurately than other known algorithms.