TY - GEN
T1 - Implications of MR contrast standardization on image computing
AU - Rajagopalan, Srinivasan
AU - Robb, Richard A.
PY - 2006
Y1 - 2006
N2 - The process of transforming the non-linear magnetic field perturbations induced by radiowaves into linear reconstructions based on Radon and Fourier transforms has resulted in MR acquisitions in which intensities do not have a fixed meaning, not even within the same protocol, for the same body region, for images obtained on the same scanner, for the same patient, on the same day. This makes robust image interpretation and processing extremely challenging. The status quo of fine tuning an image processing algorithm with the ever-varying MRI intensity space could best be summarized as a "random search through the parameter space". This work demonstrates the implications of standardizing the contrast across multiple tissue types on the robustness and efficiency of image processing algorithms. Contrast standardization is performed using a prior-knowledge driven feature-guided, fast, non-linear equalization technique. Without loss of generality, skull stripping and brain tissue segmentation are considered in this investigation. Results show that the iterative image processing algorithms converge faster with minimal parameter tweaking and the abstractions are significantly better in the contrast standardized space than in the native stochastic space.
AB - The process of transforming the non-linear magnetic field perturbations induced by radiowaves into linear reconstructions based on Radon and Fourier transforms has resulted in MR acquisitions in which intensities do not have a fixed meaning, not even within the same protocol, for the same body region, for images obtained on the same scanner, for the same patient, on the same day. This makes robust image interpretation and processing extremely challenging. The status quo of fine tuning an image processing algorithm with the ever-varying MRI intensity space could best be summarized as a "random search through the parameter space". This work demonstrates the implications of standardizing the contrast across multiple tissue types on the robustness and efficiency of image processing algorithms. Contrast standardization is performed using a prior-knowledge driven feature-guided, fast, non-linear equalization technique. Without loss of generality, skull stripping and brain tissue segmentation are considered in this investigation. Results show that the iterative image processing algorithms converge faster with minimal parameter tweaking and the abstractions are significantly better in the contrast standardized space than in the native stochastic space.
KW - Intensity standardization
KW - Magnetic Resonance Imaging
KW - Prior-information
KW - Skull stripping
KW - Tissue contrast
UR - http://www.scopus.com/inward/record.url?scp=33745166284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745166284&partnerID=8YFLogxK
U2 - 10.1117/12.653959
DO - 10.1117/12.653959
M3 - Conference contribution
AN - SCOPUS:33745166284
SN - 0819464236
SN - 9780819464231
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2006
T2 - Medical Imaging 2006: Image Processing
Y2 - 13 February 2006 through 16 February 2006
ER -