By Yonghong Shi, Shu Liao, Dinggang Shen (auth.), Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan (eds.)
This e-book constitutes the refereed complaints of the second one overseas Workshop on computing device studying in clinical Imaging, MLMI 2011, held along side MICCAI 2011, in Toronto, Canada, in September 2011. The forty four revised complete papers provided have been conscientiously reviewed and chosen from seventy four submissions. The papers specialize in significant traits in laptop studying in clinical imaging aiming to spot new state of the art concepts and their use in scientific imaging.
Read Online or Download Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings PDF
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Additional info for Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings
Sample text
A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975) 34 R. M. Resnick, and C. Davatzikos 9. : Predictive markers for ad in a multi-modality framework: An analysis of mci progression in the adni population. NeuroImage 55(2), 574–589 (2011) 10. : Amyloid beta40/42 clearance across the blood-brain barrier following intra-ventricular injections in wild-type, apoe knock-out and human apoe3 or e4 expressing transgenic mice. J. Alzheimers Dis.
Manual segmentation of the bone surface in US images is highly operator dependent and time consuming [5]. Moreover, the thickness of the bone surface can reach 4 mm in some images [1], so manual segmentation can lead to an error higher than some millimeters. Foroughi et al. [6] developed an automatic segmentation method of bone surface in US images using dynamic programming. This method depends on a threshold value. 55 seconds. In this paper, our main interest lies on the use of US images in computer assisted intramedullary nailing of tibia shaft fractures.
Figure 4(a) shows correlation between the classification values and the rate of change in CVLT for different years of evaluation In general, correlation between the classification values and rate of change in cognitive performance increases with age, which is expected given that the MKL classifier was trained on the lastvisit evaluations of the labeled individuals. Additionally, Figures 4(b) and 4(c) show evolution of correlation between the classification values and BVRT and (a) (b) (c) Fig. 4. Correlation between the value of the classification function and the rate of change in cognitive evaluations for specific evaluation years.
