Modified expectation maximization algorithm for MRI segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6419 LNCS, Issue , 2010, Pages 63-70

  Donoso, Ramiro ^a   Veloz, Alejandro ^a   Allende, Héctor ^a  

^a DEPARTAMENTO DE FÍSICA   (Chile)

Author keywords

Expectation Maximization algorithm; Finite Mixture models; Magnetic Resonance Imaging segmentation; spatial information

Indexed keywords

EXPECTATION-MAXIMIZATION ALGORITHMS; FINITE MIXTURE MODELS; MAGNETIC RESONANCE IMAGES; MODIFIED EXPECTATION-MAXIMIZATION; MRI SEGMENTATION; SIZE AND SHAPE; SPATIAL DEPENDENCE; SPATIAL INFORMATIONS; FINITE MIXTURE MODELING;

ALGORITHMS; COMPUTER VISION; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; MAXIMUM PRINCIPLE; MEDICAL APPLICATIONS; MEDICAL IMAGING; MIXTURES; OPTIMIZATION; RESONANCE; SIGNAL RECONSTRUCTION; DIAGNOSIS; PATTERN RECOGNITION;

IMAGE SEGMENTATION;

EID: 78649945680     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16687-7_13     Document Type: Conference Paper

References (11)

1. Gering, D., Nabavi, A., Kikinis, R., Hata, N., O'Donnell, L., Grimson, W., Jolesz, F., Black, P., Wells, W.: An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and an Open MR. Journal of Magnetic Resonance Imaging 13, 967-975 (2001) (Pubitemid 32479958)
2. Ambroise, C., Govaert, G.: Spatial Clustering and the EM Algorithm, Tech. report, Université de technologie de Compiègne, France (1996)
3. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of The Royal Statistical Society, Series B 39(1), 1-38 (1977)
4. Gil, P., Torres, F., Ortiz, F.G.: Detección de objetos por segmentación multinivel combinada de espacios de color, Tech. report, Dpto. Física, Ingeniería de Sistemas y Teoría de la Señal. Universidad de Alicate (Septiembre 2004)
5. Hu, T., Sung, S.Y.: Clustering Spatial Data with a Hybrid EM Approach, Tech. report, Department of Computer Science, National University of Singapore (2005)
6. Kaus, M.: Contributions to the Automated Segmentation of Brain Tumors in Magnetic Resonance Images, Ph.D. thesis, Der Technischen Fakultat der Universitat Erlangen-Nurnberg (1999)
7. Mostafa, M.G., Tolba, M.F., Gharib, T.F., Megeed, M.A.: Medical Image Segmentation Using a Wavelet-Based Multiresolution EM Algorithm. In: IEEE International Conference on Industrial Electronics, Technology and Automation, December 19-21 (2001)
8. Veloz, A., Chabert, S., Salas, R., Orellana, A., Vielma, J.: Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 861-870. Springer, Heidelberg (2007)
9. Yang, Z., Chung, F.-L., Shitong, W.: Robust fuzzy clustering based image segmentation. Applied Soft Computing 9, 80-84 (2008)
10. Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transactions on Medical Imaging 20(1), 45-57 (2001)
11. Zhang, Y., Gerbrands, J.: Segmentation Evaluation Using Ultimate Measurament Accuracy. Image Processing Algorithms and Techniques III 1657, 449-460 (1996)