Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7966, Issue , 2011, Pages

  Kalayeh, Mahdi M ^a   Marin, Thibault ^a   Pretorius, P Hendrik ^b   Wernick, Miles N ^a   Yang, Yongyi ^a   Brankov, Jovan G ^a  

^a Illinois Institute of Technology   (United States)

^b UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

Author keywords

AUC; Bayesian learning; MSE; Numerical observer; Perfusion defect detection; RBF kernels; Relevance vector machine; SPECT

Indexed keywords

AUC; BAYESIAN LEARNING; MSE; NUMERICAL OBSERVER; PERFUSION DEFECT DETECTION; RBF KERNELS; RELEVANCE VECTOR MACHINE; SPECT;

DEFECTS; FORECASTING; IMAGE QUALITY; RATING; SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY; TOMOGRAPHY; VECTORS;

MEDICAL IMAGING;

EID: 79955857280     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.878176     Document Type: Conference Paper

References (15)

1. Myers, K. J. and Barrett, H. H., "Addition of a channel mechanism to the ideal-observer model, " Journal of the Optical Society of America A 4, 2447-2457 (1987).
2. Yao, J. and Barrett, H. H., "Predicting human performance by a channelized Hotelling observer model, " in [Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series], Wilson, D. C. and Wilson, J. N., eds., Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference 1768, 161-168 (1992). (Pubitemid 23617152)
3. Abbey, C. K. and Barrett, H. H., "Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability, " Journal of the Optical Society of America A 18, 473-488 (2001).
4. Brankov, J. G., Yang, Y., Wei, L., Naqa, I. E., and Wernick, M. N., "Learning a channelized observer for image quality assessment, " IEEE Transactions on Medical Imaging 28, 991-999 (2009).
5. Vapnik, V. N., [Statistical learning theory], Wiley, New-York, NY (1998).
6. Tipping, M. E., "Sparse Bayesian learning and the relevance vector machine, " Journal of Machine Learning Research 1, 211-244 (2001). (Pubitemid 33687203)
7. Fletcher, T., "Relevance Vector Machines Explained, " a tutorial on RVM (2008).
8. Hanley, J. A., McNeil, B. J., "The meaning and use of the area under a receiver operating characteristic (ROC) curve, " Radiology 143, 29-36 (1982). (Pubitemid 12142173)
9. Barrett, H. H. and Myers, K. J., [Foundations of image science], Wiley-Interscience, Hoboken, NJ (2004).
10. MOSEK ApS, "The MOSEK optimization software, " [Available:http://www.mosek.com] (1997 [Accessed Apr 2010]).
11. Weston, J., Elisseeff, A., Bakir, G., and Sinz, F., "The spider machine learning toolbox, " [Available:http://www.kyb.tuebingen.mpg.de/bs/ people/spider] (2005 [Accessed Apr 2010]).
12. Narayanan, M. V., Gifford, H. C., King, M. A., Pretorius, P. H., Farncombe, T. H., Bruyant, P. P. and Wernick, M. N., "Optimization of iterative reconstructions of 99mTc cardiac SPECT studies using numerical observers, " IEEE Transactions on Nuclear Science 49, 2355-2360 (2002). (Pubitemid 35443101)
13. Pretorius, P. H., King, M. A., Tsui, B. M. W., LaCroix, K. J., and Xia, W., "A mathematical model of motion of the heart for use in generating source and attenuation maps for simulating emission imaging, " Medical Physics 26, 2323-2332 (1999).
14. Ljungberg, M. and Strand, S.-E., "A Monte Carlo program for the simulation of scintillation camera characteristics, " Computer Methods and Programs in Biomedicine 29, 257-272 (1989). (Pubitemid 20610320)
15. Hudson, H. M., and Larkin, R. S., "Accelerated image reconstruction using ordered subsets of projection data, " IEEE Transaction on Medical Imaging 13.4, 601-609 (1994).