Dr. Chris A. Cocosco Profile

Dr. Chris A. Cocosco

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Publications (5)

Proceedings Article | 15 March 2006 Paper

kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training

Henri Vrooman, Chris Cocosco, Rik Stokking, M. Arfan Ikram, Meike Vernooij, Monique Breteler, Wiro Niessen

Proceedings Volume 6144, 61443L (2006) https://doi.org/10.1117/12.650522

KEYWORDS: Tissues, Brain, Image segmentation, Magnetic resonance imaging, Electronic filtering, Image processing, Neuroimaging, Image classification, Brain mapping, Data acquisition

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Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue, requires laborious training on manually labeled subjects. In this work, the performance of kNN-based segmentation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using manual training is compared with a new method, in which training is automated using an atlas. From 12 subjects, standard T2 and PD scans and a high-resolution, high-contrast scan (Siemens T1-weighted HASTE sequence with reverse contrast) were used as feature sets. For the conventional kNN method, manual segmentations were used for training, and classifications were evaluated in a leave-one-out study. The performance as a function of the number of samples per tissue, and k was studied. For fully automated training, scans were registered to a probabilistic brain atlas. Initial training samples were randomly selected per tissue based on a threshold on the tissue probability. These initials were processed to keep the most reliable samples. Performance of the method for varying the threshold on the tissue probability method was studied. By measuring the percentage overlap (SI), classification results of both methods were validated. For conventional kNN classification, varying the number of training samples did not result in significant differences, while increasing k gave significantly better results. In the method using automated training, there is an overestimation of GM at the expense of CSF at higher thresholds on the tissue probability maps. The difference between the conventional method (k=45) and the observers was not significantly larger than inter-observer variability for all tissue types. The automated method performed slightly worse and performed equal to the observers for WM, and less for CSF and GM. From these results it can be concluded that conventional kNN classification may replace manual segmentation, and that atlas-based kNN segmentation has strong potential for fully automated segmentation, without the need of laborious manual training.

Proceedings Article | 10 March 2006 Paper

Automatic cardiac MRI myocardium segmentation using graphcut

Gunnar Kedenburg, Chris Cocosco, Ullrich Köthe, Wiro Niessen, Evert-jan Vonken, Max Viergever

Proceedings Volume 6144, 61440A (2006) https://doi.org/10.1117/12.653583

KEYWORDS: Image segmentation, Fuzzy logic, Cardiovascular magnetic resonance imaging, Tissues, Magnetic resonance imaging, Lung, Blood, Statistical modeling, Heart, Data acquisition

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Proceedings Article | 16 September 2005 Paper

Automatic image-driven segmentation of cardiac ventricles in cine anatomical MRI

Chris Cocosco, Wiro Niessen, Thomas Netsch, Evert-jan Vonken, Max Viergever

Proceedings Volume 5909, 59091M (2005) https://doi.org/10.1117/12.625575

KEYWORDS: Image segmentation, Magnetic resonance imaging, Heart, Medical imaging, Visualization, Image processing algorithms and systems, Image quality, Data centers, Binary data, Inspection

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Proceedings Article | 12 May 2004 Paper

Model-based segmentation of cardiac MRI cine sequences: a Bayesian formulation

Julien Senegas, Chris Cocosco, Thomas Netsch

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.534073

KEYWORDS: Image segmentation, Motion models, Data modeling, Heart, Magnetic resonance imaging, Expectation maximization algorithms, Cardiovascular magnetic resonance imaging, Monte Carlo methods, 3D modeling, Tissues

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Proceedings Article | 12 May 2004 Paper

Slice-to-volume registration using mutual information between probabilistic image classifications

Adam Chandler, Thomas Netsch, Chris Cocosco, Julia Schnabel, David Hawkes

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.534966

KEYWORDS: Image registration, Magnetic resonance imaging, 3D image processing, Brain, Image classification, Fuzzy logic, Image restoration, 3D acquisition, Medical imaging, Rigid registration

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