publication . Article . 2012

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Sung Wook Baik; Irfan Mehmood; Ghazanfar Latif;
Open Access English
  • Published: 01 Jun 2012 Journal: International Journal of Computational Intelligence Systems (issn: 1875-6883, Copyright policy)
  • Publisher: Atlantis Press
Abstract
In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of ...
Subjects
Medical Subject Headings: genetic structures
free text keywords: Magnetic resonance imaging (MRI), Classification, Image Segmentation, Tumor detection, 3D reconstruction, Electronic computers. Computer science, QA75.5-76.95, General Computer Science, Computational Mathematics
38 references, page 1 of 3

[1] Pal, N.R. and S.K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9): 1277-1294,1993.

[2] Milan Sonka, Satish K. Tadikonda, Steve M. Collins,Know1edge-Based Interpretation of MR Brain Images , in IEEE Transaction on Medical Imaging, Vol. 15, no. 4, pp. 443-452, Aug 1996.

[3] W. Grimson, G. Ettinger, T. Kapur, M. Leventon, W. Wells, R. Kikinis, Utilizing segmented MRI data in image-guided surgery, International Journal of Pattern Recognition and Artificial Intelligence 11 (8) (1998) 1367-1397.

E. Grimson, M. Leventon, G. Ettinger, A. Chabrerie, S. Nakajima, F. Ozlen, H. Atsumi, R. Kikinis, P. Black, Clinical Experience with a High Precision Image-Guided Neurosurgery System, MICCAI, Springer, Berlin, 1998. pp. 63-73. [OpenAIRE]

A. Liew, and H. Yan, “Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images,” Current Medical Imaging Reviews, vol. 2, 2006, pp. 91-103, doi: 10.1118/1.1569270.

N. Duta, and M. Sonka, “Segmentation and Interpretation of MR Brain Images: An Improved Active Shape Model,” IEEE Trans. Med. Imag., vol. 17, Dec. 1998, pp. 1049-1062, doi: 10.1109/42.746716 H. Li, A. Yezzi, and L. D. Cohen, “Fast 3D Brain Segmentation Using Dual-Front Active Contours with Optional User-Interaction,” Proc. International Workshop on Computer Vision for Biomedical Image Applications, Lecture Notes in Computer Science, vol. 3765, 2005, pp. 335-345, doi: 10.1007/11569541_34 R. Pohle, and K.D. Toennies, “Segmentation of Medical Images Using Adaptive Region Growing,” Proc. SPIE, Medical Imaging, vol. 4322, 2001, pp. 1337-1346.

[9] I.N. Manousakas, P.E. Undrill, G.G. Cameron, and T.W. Redpath, “Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions,” Computers and Biomedical Research, vol. 31, Dec. 1998, pp. 393-412, doi:10.1006/cbmr.1998.1489

[10] G. Bueno, O. Musse, F. Heitz, and J. P. Armspach, “3D Watershedbased Segmentation of Internal Structures within MR Brain Images,” Proc. SPIE, Medical Imaging 2000: Image Processing, vol. 3979, 2000, pp. 284-293.

[11] K. Held, E.R. Kops, B.J. Krause, W.M. Wells, R. Kikinis, and H.W. Muller-Gartner, “Markov Random Field Segmentation of Brain MR Images,” IEEE Trans. Med. Imag., vol. 16, Dec. 1997, pp. 878-886, doi: 10.1109/42.650883

[12] M. Ibrahim, N. John, M. Kabuka, and A. Younis, “Hidden Markov models-based 3D MRI brain segmentation,” Image and Vision Computing, vol. 24, Oct. 2006, pp. 1065-1079, doi:10.1016/j.imavis.2006.03.001

[13] M. Desco, J. D. Gispert, S. Reig, A. Santos, J. Pascau, N. Malpica, P. Garcia-Barreno, “Statistical Segmentation of Multidimensional Brain Datasets,” Proc. SPIE, Medical Imaging 2001: Image Processing, vol. 4322, Jul. 2001, pp. 184-193, doi:10.1117/12.431071 [OpenAIRE]

[14] R. Sammouda, N. Niki, and H. Nishitani, “A Comparison of Hopfield Neural Network and Boltzmann Machine in Segmenting MR Images of the Brain,” IEEE Trans. Nucl. Sci., vol. 43, Dec. 1996, pp. 3361-3369, doi: 10.1109/NSSMIC.1995.510462

[15] J. Zhou, K. L. Chan, V. F. H. Chongand, and S. M. Krishnan, “Extraction of Brain Tumor from MR Images Using One-class Support Vector Machine,” Proc. IEEE Engineering in Medicine and Biology Society (EMBS 05), 2005, pp. 6411-6414.

[16] S. Shen, W. Sandham, M. Granat, and A. Sterr, “MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction with Neural-Network Optimization,” IEEE Trans. Med. Imag., vol. 9, Sep. 2005, pp. 459-467, doi: 10.1109/TITB.2005.847500

[17] W. R. Crum, “Spectral Clustering and Label Fusion for 3D Tissue Classification: Sensitivity and Consistency Analysis,” Proc. Medical Image Understanding and Analysis, July, 2008, pp. 149-153.

38 references, page 1 of 3
Abstract
In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of ...
Subjects
Medical Subject Headings: genetic structures
free text keywords: Magnetic resonance imaging (MRI), Classification, Image Segmentation, Tumor detection, 3D reconstruction, Electronic computers. Computer science, QA75.5-76.95, General Computer Science, Computational Mathematics
38 references, page 1 of 3

[1] Pal, N.R. and S.K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9): 1277-1294,1993.

[2] Milan Sonka, Satish K. Tadikonda, Steve M. Collins,Know1edge-Based Interpretation of MR Brain Images , in IEEE Transaction on Medical Imaging, Vol. 15, no. 4, pp. 443-452, Aug 1996.

[3] W. Grimson, G. Ettinger, T. Kapur, M. Leventon, W. Wells, R. Kikinis, Utilizing segmented MRI data in image-guided surgery, International Journal of Pattern Recognition and Artificial Intelligence 11 (8) (1998) 1367-1397.

E. Grimson, M. Leventon, G. Ettinger, A. Chabrerie, S. Nakajima, F. Ozlen, H. Atsumi, R. Kikinis, P. Black, Clinical Experience with a High Precision Image-Guided Neurosurgery System, MICCAI, Springer, Berlin, 1998. pp. 63-73. [OpenAIRE]

A. Liew, and H. Yan, “Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images,” Current Medical Imaging Reviews, vol. 2, 2006, pp. 91-103, doi: 10.1118/1.1569270.

N. Duta, and M. Sonka, “Segmentation and Interpretation of MR Brain Images: An Improved Active Shape Model,” IEEE Trans. Med. Imag., vol. 17, Dec. 1998, pp. 1049-1062, doi: 10.1109/42.746716 H. Li, A. Yezzi, and L. D. Cohen, “Fast 3D Brain Segmentation Using Dual-Front Active Contours with Optional User-Interaction,” Proc. International Workshop on Computer Vision for Biomedical Image Applications, Lecture Notes in Computer Science, vol. 3765, 2005, pp. 335-345, doi: 10.1007/11569541_34 R. Pohle, and K.D. Toennies, “Segmentation of Medical Images Using Adaptive Region Growing,” Proc. SPIE, Medical Imaging, vol. 4322, 2001, pp. 1337-1346.

[9] I.N. Manousakas, P.E. Undrill, G.G. Cameron, and T.W. Redpath, “Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions,” Computers and Biomedical Research, vol. 31, Dec. 1998, pp. 393-412, doi:10.1006/cbmr.1998.1489

[10] G. Bueno, O. Musse, F. Heitz, and J. P. Armspach, “3D Watershedbased Segmentation of Internal Structures within MR Brain Images,” Proc. SPIE, Medical Imaging 2000: Image Processing, vol. 3979, 2000, pp. 284-293.

[11] K. Held, E.R. Kops, B.J. Krause, W.M. Wells, R. Kikinis, and H.W. Muller-Gartner, “Markov Random Field Segmentation of Brain MR Images,” IEEE Trans. Med. Imag., vol. 16, Dec. 1997, pp. 878-886, doi: 10.1109/42.650883

[12] M. Ibrahim, N. John, M. Kabuka, and A. Younis, “Hidden Markov models-based 3D MRI brain segmentation,” Image and Vision Computing, vol. 24, Oct. 2006, pp. 1065-1079, doi:10.1016/j.imavis.2006.03.001

[13] M. Desco, J. D. Gispert, S. Reig, A. Santos, J. Pascau, N. Malpica, P. Garcia-Barreno, “Statistical Segmentation of Multidimensional Brain Datasets,” Proc. SPIE, Medical Imaging 2001: Image Processing, vol. 4322, Jul. 2001, pp. 184-193, doi:10.1117/12.431071 [OpenAIRE]

[14] R. Sammouda, N. Niki, and H. Nishitani, “A Comparison of Hopfield Neural Network and Boltzmann Machine in Segmenting MR Images of the Brain,” IEEE Trans. Nucl. Sci., vol. 43, Dec. 1996, pp. 3361-3369, doi: 10.1109/NSSMIC.1995.510462

[15] J. Zhou, K. L. Chan, V. F. H. Chongand, and S. M. Krishnan, “Extraction of Brain Tumor from MR Images Using One-class Support Vector Machine,” Proc. IEEE Engineering in Medicine and Biology Society (EMBS 05), 2005, pp. 6411-6414.

[16] S. Shen, W. Sandham, M. Granat, and A. Sterr, “MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction with Neural-Network Optimization,” IEEE Trans. Med. Imag., vol. 9, Sep. 2005, pp. 459-467, doi: 10.1109/TITB.2005.847500

[17] W. R. Crum, “Spectral Clustering and Label Fusion for 3D Tissue Classification: Sensitivity and Consistency Analysis,” Proc. Medical Image Understanding and Analysis, July, 2008, pp. 149-153.

38 references, page 1 of 3
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