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

Henri A. Vrooman; Chris A. Cocosco; Rik Stokking; M. Arfan Ikram; Meike W. Vernooij; Monique M. Breteler; Wiro J. Niessen

doi:10.1117/12.650522

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15 March 2006 kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training

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

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Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61443L (2006) https://doi.org/10.1117/12.650522
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

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.

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Henri A. Vrooman, Chris A. Cocosco, Rik Stokking, M. Arfan Ikram, Meike W. Vernooij, Monique M. Breteler, and Wiro J. Niessen "kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61443L (15 March 2006); https://doi.org/10.1117/12.650522

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