Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines

International Journal of Computer Assisted Radiology and Surgery

Volumn 6, Issue 2, 2011, Pages 163-174

  Zuluaga, Maria A ^a,b   Magnin, Isabelle E ^b   Hernández Hoyos, Marcela ^a   Delgado Leyton, Edgar J F ^a   Lozano, Fernando ^a   Orkisz, Maciej ^b  

^a UNIVERSIDAD DE LOS ANDES   (Colombia)

^b UNIVERSITÉ DE LYON   (France)

Author keywords

Atherosclerosis; Computed tomography; Computer assisted diagnosis; Density level detection; Support vector machines; Vascular disease

Indexed keywords

ARTICLE; AUTOMATION; CALCIFICATION; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DENSITY LEVEL DETECTION; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE QUALITY; INTERMETHOD COMPARISON; MACHINE LEARNING; PHANTOM; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TIME; VALIDATION STUDY; VASCULAR DISEASE;

EID: 79955937824     PISSN: 18616410     EISSN: 18616429     Source Type: Journal    
DOI: 10.1007/s11548-010-0494-8     Document Type: Article

References (29)

1. Adame IM, van der Geest RJ, Wasserman B, Mohamed M, Reiber JHC, Lelieveldt BPF (2004) Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries. In: Fitzpatrick JM, Sonka M (eds) Proceedings of SPIE Med. Imaging 2004: image processing, vol 5370. San Diego, pp 265-273
2. Barnett V, Lewis T (1994) Outliers in Statistical Data, 3rd edn. John Wiley & Sons, West Sussex
3. Bellman R (1961) Adaptive control processes. Princeton University Press, New Jersey
4. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
5. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37-46. doi:10.1177/ 001316446002000104
6. Dehmeshki J, Ye X, Wang F, Lin XY, Abaei M, Siddique MM, Qanadli SD (2004) An accurate and reproducible scheme for quantification of coronary artery calcification in ct scans. In: Proceedings of 26th annual international conference IEEE EMBS. San Francisco, California, USA, pp 1918-1921
7. Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378-382
8. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp SL (eds)Medical image computing and computer-assisted intervention - MICCAI'98, LectureNotes in Computer Science, vol 1496, Springer Verlag, Berlin, Germany, pp 130-137
9. Hernández Hoyos M, Orkisz M, Douek PC, Magnin IE (2005) Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improved generation of the centerline. Mach Graph Vis 14(4):349-378 (Pubitemid 44199013)
10. Hernández Hoyos M, Serfaty JM, Maghiar A, Mansard C, Orkisz M, Magnin IE, Douek PC (2006) Evaluation of semi-automatic arterial stenosis quantification. Int J Comput Assist Radiol Surg 1(3):167-175 (Pubitemid 44819268)
11. Hush D, Kelly P, Scovel C, Steinwart I (2005) Provably fast algorithms for anomaly detection. Technical Report LA-UR-05-4367, Los Alamos National Laboratory
12. Išgum I, Rutten A, Prokop M, van Ginneken B (2007) Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. Med Phys 34(4):1450-1461 (Pubitemid 46555775)
13. Kang DG, Suh DC, Ra JB (2009) Three-dimensional blood vessel quantification via centerline deformation. IEEE Trans Med Imaging 28(3):405-414
14. Krissian K, Maladain G, Ayache N (2000) Model based detection of tubular structures in 3D images. Comput Vis Image Unders 80:130-171 (Pubitemid 32021582)
15. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159-174 (Pubitemid 8054849)
16. Lekadir K, Yang GZ (2006) Carotid artery segmentation using an outlier immune 3D active shape models framework. In: Larsen R, Nielsen M, Sporring J (eds) MICCAI 2006, LNCS 4190. Springer, Berlin, Heidelberg, Copenhagen, pp 620-627
17. Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med Image Anal 13(6):819-845
18. Renard F, Yang Y (2008) Image analysis for detection of coronary artery soft plaques in MDCT images. In: 2008 IEEE International Symposium Biomed. Imaging: from nano to macro (ISBI 2008). Paris, France, pp 25-28
19. Rinck D, Krüger S, Reimann A, Scheuering M (2006) Shape-based segmentation and visualization techniques for evaluation of atherosclerotic plaques in coronary artery disease. In: Cleary KR, Galloway Jr RL (eds) Proceedings of SPIE Med. Imaging 2006: Visualization, image-guided procedures and display, vol 6141, pp 124-132
20. Salvado O (2006) Characterization of atherosclerosis with magnetic resonance imaging, challenges and validation. Ph.D. thesis, Case Western Reserve University
21. Schaap M, Metz C, vanWalsum T, van der Giessen AG,Weustink AC, Mollet NR, Bauer C, Bogunovic H, Castro C, Deng X, Dikici E, O'Donnell T, Frenay M, Friman O, Hernández Hoyos M, Kitslaar PH, Krissian K, Kühnel C, Luengo-Oroz MA, Orkisz M, Smedby Ö, Styner M, Szymczak A, Tek H, Wang C, Warfield SK, Zambal S, Zhang Y, Krestin GP, Niessen WJ (2009) Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 13(5):701-714
22. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ,Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443-1471 (Pubitemid 33595028)
23. Steinwart I, Hush D, Scovel C (2005) A classification framework for anomaly detection. J Mach Learn Res 6:211-232
24. Steinwart I, Hush D, Scovel C (2005) Density level detection is classification. Neural Inf Process Syst 17:1337-1344
25. Vukadinovic D, vanWalsum T, Rozie S, deWeert T, Manniesing R, van der Lugt A, Niessen WJ (2009) Carotid artery segmentation and plaque quantification in CTA. In: Proceedings of 6th IEEE International Symposium Biomed. Images (ISBI'09). Boston, USA, pp 835-838
26. Wong WCK, Chung ACS (2006) Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning. IEEE Trans. Med. Imaging 25(6):665-684
27. World Health Organization: The top ten causes of death-fact sheet N310 (2008)
28. Wörz S, Rohr K (2007) Segmentation and quantification of human vessels usind a 3D cylindrical intensity model. IEEE Trans Image Process 16(8):1994-2004 (Pubitemid 47225451)
29. Zhao F, Zhang H,Wahle A, Thomas MT, Stolpen AH, Scholz TD, Sonka M (2009) Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis. Med Image Anal 13(3):483-493