Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET

Physics in Medicine and Biology

Volumn 52, Issue 12, 2007, Pages 3467-3491

  Hatt, M ^a   Lamare, F ^a   Boussion, N ^a   Turzo, A ^a,b   Collet, C ^c   Salzenstein, F ^d   Roux, C ^a,e   Jarritt, P ^f   Carson, K ^f   Rest, C Cheze Le ^a,b   Visvikis, D ^a  

^a INSERM   (France)

^b CHU MORVAN   (France)

^c UNIVERSITÉ DE STRASBOURG   (France)

^d Faculté de Médecine   (France)

^e ECOLE DES MINES DE NANTES   (France)

^f ROYAL VICTORIA HOSPITAL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; FUZZY SYSTEMS; IMAGE SEGMENTATION; MARKOV PROCESSES; MATHEMATICAL MODELS; POSITRON EMISSION TOMOGRAPHY; RADIOTHERAPY;

FUZZY HIDDEN MARKOV CHAINS (FHMC); HIDDEN MARKOV CHAIN (HMC) ALGORITHMS; IMAGE NOISE; LESION SIZES; VOXEL SIZES;

ONCOLOGY;

ACCURACY; ALGORITHM; ARTICLE; CANCER RADIOTHERAPY; CORRELATION ANALYSIS; ERROR; FUZZY SYSTEM; IMAGE RECONSTRUCTION; METHODOLOGY; NOISE; PHANTOM; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; PROBABILITY;

EID: 34249749945     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/52/12/010     Document Type: Article

References (29)

1. Benmiloud B and Pieczynski W 1995 Estimation des paramètres dans les chaînes de Markov cachées et segmentation d'images Traitement du signal 12 433-54
2. Celeux G and Diebolt J 1986 L'algorithme SEM: un algorithme d'apprentissage probabiliste pour la reconnaissance de mélanges de densités Revue de Statistique Appliquée 34 35-52
3. Chen J L, Gunn S R and Nixon M S 2001 Markov random field model for segmentation of PET images Lecture Notes Comput. Sci. 2082 468-74
4. Dai J 1994 Hybrid approach to speech recognition using hidden Markov models and Markov chains Proc. Inst. Electron. Eng: Vis. Image Signal Process. 141 273-9
5. Delignon Y, Marzouki A and Pieczynski W 1997 Estimation of generalized mixtures and its application in image segmentation IEEE Trans. Image Process. 6 1364-75
6. Dempster A P, Laird N M and Rubin D B 1977 Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc. B 39 1-38
7. Erdi Y E, Mawlawi O, Larson S M, Imbriaco M, Yeung H, Finn R and Humm J L 1997 Segmentation of lung lesion volume by adaptative positron emission tomography image thresholding Cancer 80 2505-9
8. Hatt M, Roux C and Visvikis D 2007 3D fuzzy adaptive unsupervised Bayesian segmentation for volume determination in PET 4th IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro (Arlington, USA)
9. International Electrotechnical Commission (IEC) 1998 IEC Publication 61675-1: Radionuclide Imaging Devices-Characteristics and Test Conditions: Part 1. Position Emission Tomographs
10. Jarritt H, Carson K, Hounsel A R and Visvikis D 2006 The role of PET/CT scanning in radiotherapy planning Br. J. Radiol. 79 S27-35
11. Kamata S, Eason R O and Bandou Y 1999 A new algorithm for N-dimensional Hilbert scanning IEEE Trans. Image Process. 8 964-73
12. Kim J, Feng D D, Cai T W and Eberl S 2002 Automatic 3D temporal kinetics segmentation of dynamic emission tomography image using adaptative region growing cluster analysis IEEE NSS-MIC Conf. Rec. vol 3 pp 1580-3
13. Krak N C et al 2005 Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial Eur. J. Nucl. Med. Mol. Imaging 32 294-301
14. Lamare F, Turzo A, Bizais Y, Cheze Le Rest C and Visvikis D 2006 Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE Phys. Med. Biol. 51 943-62
15. Lanchantin P and Pieczynski W 2004 Unsupervised non stationary image segmentation using triplet Markov chains Advanced Concepts for Intelligent Vision Systems (ACVIS 04) (Belgium)
16. Marroquin J, Mitter S and Poggio T 1987 Probabilistic solution of ill-posed problems in computational vision J. Am. Stat. Assoc. 82 76-89
17. McQueen J 1967 Some methods for classification and analysis of multivariate observations Proc. the 5th Berkeley Symp. on Mathematical Statistics and Probability vol 1 pp 281-97
18. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welch C, Hellwig D, Rübe C and Kirsch C M 2005 Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer J. Nucl. Med. 46 1342-8
19. Pieczynski W 2003 Modèles de Markov en traitement d'images Traitement du Signal 20 255-77
20. Pieczynski W and Derode S 2004 Signal and image segmentation using pairwise Markov chains IEEE Trans. Signal Process. 52 2477-89
21. Reader A J et al 2002 One-pass list-mode EM algorithm for high-resolution 3D PET image reconstruction into large array IEEE Trans. Nucl. Sci. 49 693-9
22. Reutter B W, Klein G J and Huesman R H 1997 Automated 3D segmentation of respiratory-gated PET transmission images IEEE Trans. Nucl. Sci. 44 2473-6
23. Riddell C, Brigger P, Carson R E and Bacharach S L 1999 The watershed algorithm: a method to segment noisy PET transmission images IEEE Trans. Nucl. Sci. 46 713-19
24. Salzenstein F, Collet C and Petremand M 2004 Champs de Markov flous pour images multispectrales Traitement du Signal 21 37-54
25. Salzenstein F and Pieczynski W 1997 Parameter estimation in hidden fuzzy Markov random fields and image segmentation Graph. Models Image Process. 59 205-20
26. Salzenstein F and Pieczynski W 1998 Sur le choix de méthode de segmentation statistique d'images Traitement du Signal 15 119-28
27. Visvikis D, Turzo A, Gouret S, Damine P, Lamare F, Bizais Y, Cheze Le and Rest C 2004 Characterisation of SUV accuracy in FDG PET using 3D RAMLA and the Philips Allegro PET scanner J. Nucl. Med. 45 103P
28. Visvikis D et al 2001 Influence of OSEM and segmented attenuation correction in the calculation of standardised uptake values for FDG PET Eur. J. Nucl. Med. Mol. Imaging 28 1326-35
29. Zhu W and Jiang T 2003 Automation segmentation of PET image for brain tumors IEEE Nuclear Science Symp. Conf. Rec.-NSS-MIC vol 4 pp 2627-9