Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction

The concentration edge -detection and Gegenbauer image-reconstruction methods were previously shown to improve the quality of segmentation in magnetic resonance imaging. In this study, these methods are utilized as a pre-processing step to the Weibull E-SD field segmentation. It is demonstrated that the combination of the concentration edge detection and Gegenbauer reconstruction method improves the accuracy of segmentation for the simulated test data and real magnetic resonance images used in this study.

[1]  D. Gottlieb,et al.  Numerical analysis of spectral methods : theory and applications , 1977 .

[2]  Victor E. DeBrunner,et al.  Texture-based segmentation of satellite weather imagery , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Thomas C. M. Lee.  Segmenting Images Corrupted by Correlated Noise , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Steve R. Gunn,et al.  Markov Random Field Models for Segmentation of PET Images , 2001, IPMI.

[6]  Steven Senger.  Visualizing and Segmenting Large Volumetric Data Sets , 1999, IEEE Computer Graphics and Applications.

[7]  Kewei Chen,et al.  Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method , 2003, NeuroImage.

[8]  Yuan Yan Tang,et al.  Adaptive Image Segmentation With Distributed Behavior-Based Agents , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Rick Archibald,et al.  A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity , 2002, IEEE Transactions on Medical Imaging.

[10]  Michael Spann,et al.  Unsupervised Segmentation of 3D and 2D Seismic Reflection Data , 1999, Int. J. Pattern Recognit. Artif. Intell..

[11]  David N. Kennedy,et al.  Neuroanatomical Segmentation in MRI: Technological Objectives , 1997, Int. J. Pattern Recognit. Artif. Intell..

[12]  Anne Gelb,et al.  Detection of Edges in Spectral Data II. Nonlinear Enhancement , 2000, SIAM J. Numer. Anal..

[13]  P. Taylor.  Invited review: computer aids for decision-making in diagnostic radiology--a literature review. , 1995, The British journal of radiology.

[14]  Alex Solomonoff,et al.  On the Gibbs phenomenon I: recovering exponential accuracy from the Fourier partial sum of a nonperiodic analytic function , 1992 .

[15]  Rick Archibald,et al.  Reducing the Effects of Noise in Image Reconstruction , 2002, J. Sci. Comput..

[16]  Anne Gelb.  Parameter Optimization and Reduction of Round Off Error for the Gegenbauer Reconstruction Method , 2004, J. Sci. Comput..

[17]  Chi-Wang Shu,et al.  On the Gibbs Phenomenon and Its Resolution , 1997, SIAM Rev..

[18]  Anne Gelb,et al.  Detection of Edges in Spectral Data , 1999 .

[19]  Bhabatosh Chanda,et al.  A modified scheme for segmenting the noisy images , 1988, IEEE Trans. Syst. Man Cybern..

[20]  E. Bratsolis,et al.  SOLAR IMAGE SEGMENTATION BY USE OF MEAN FIELD FAST ANNEALING , 1998 .

[21]  Anshuman Razdan,et al.  Volumetric Segmentation Using Weibull E-SD Fields , 2003, IEEE Trans. Vis. Comput. Graph..

[22]  José M. N. Leitão,et al.  Wall position and thickness estimation from sequences of echocardiographic images , 1996, IEEE Trans. Medical Imaging.

[23]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[24]  Jing Z. Liu,et al.  Simultaneous measurement of human joint force, surface electromyograms, and functional MRI-measured brain activation , 2000, Journal of Neuroscience Methods.

[25]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..