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Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images

By Oleksandr P. Dzyubak and Erik L. Ritman


The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 μm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries

Topics: Research Article
Publisher: Hindawi Publishing Corporation
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Provided by: PubMed Central

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  1. (1997). 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,”
  2. (1986). A .L .Y u i l l ea n dT .A .P o g g i o ,“ S c a l i n gt h e o r e m sf o rz e r o -crossings,”
  3. (1986). A computational approach to edge detection,”
  4. (2005). A cross-platform freeware tool for digital reconstructionofneuronalarborizationsfromimagestacks,”Neuroinformatics,
  5. (1997). A multi-scale line filter with automatic scale selection basedontheHessianmatrixformedicalimagesegmentation,”
  6. (2008). A novel method for generating scale space kernels based on wavelet theory,”
  7. (1997). A review of nonlinear diffusion filtering,”
  8. (2009). Airway tree reconstruction based on tube detection,”
  9. (1996). Algorithms for Image Processing and Computer Vision,
  10. (2004). An Introduction to GCC, Network Theory,
  11. (1996). Blind image deconvolution,”
  12. (2010). D.Marr,Vision:AComputationalInvestigationintotheHuman Representation and Processing of Visual Information,
  13. Debian Linux Operating System,”
  14. (2007). Digital Image Processing, Prentice Hall, Upper Saddle River,
  15. (1976). Early processing of visual information,”
  16. (1998). Edge detection and ridge detection with automatic scale selection,”
  17. edition,
  18. Eds., Scale-Space Theory in Computer Vision,
  19. (1998). Feature detection with automatic scale selection,”
  20. (2004). Fitting of spatio-temporal receptivefieldsbysumsofGaussiancomponents,”Neurocomputing,
  21. (2005). Flux driven automatic centerline extraction,”
  22. (2007). Flux driven medial curve extraction,”
  23. (1988). Fronts propagating with curvature-dependent speed: algorithms based on
  24. (2007). Full affine wavelets are scale-space with twist,”
  25. (2006). Fundamental Papers in Wavelet Theory,
  26. Generalizing vesselness with respect to dimensionality and shape,” 2007,
  27. (1992). Generic neighborhood operators,”
  28. (2000). High-resolution computed tomography from efficient sampling,”
  29. (2004). Image quality assessment: from error visibility to structural similarity,”
  30. (1995). Image quality measures and their performance,”
  31. (2007). Implementation of a 3D thinning algorithm,”
  32. (2004). Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis,A
  33. (1992). l o r a c k ,B .M .t e rH a a rR o m e n y ,J .J .K o e n d e r i n k
  34. (1994). l o r a c k ,B .M .t e rH a a rR o m e n y ,J .J .K o e n d e r i n k ,a n d
  35. (1999). Level Set Methods and Fast Marching Methods: Evolving Interfaces
  36. (2009). Mean squared error: lot it or leave it? A new look at signal fidelity measures,”
  37. (1998). Mersenne twister: a 623-dimensionallyequidistributeduniformpseudo-randomnumber generator,”
  38. (2004). Micro-computed tomography—current status and developments,”
  39. (2006). Modern Image Quality Assessment,
  40. (1999). Morphology of single olivocerebellar axons labeled with biotinylated dextran amine in the rat,”
  41. (1998). Multiscale vessel enhancement filtering,”
  42. (2010). Noise simulation,”
  43. (1995). Nonlinear scale-space,”
  44. (2004). On the axioms of scale space theory,”
  45. (2004). On the influence of scale selection on feature detection for the case of linelike structures,”
  46. (1996). Pthreads Programming: A POSIX Standard for Better Multiprocessing,
  47. (1987). Representations based on zero-crossings in scale-space,”
  48. (1987). Retinal mechanisms,”
  49. (2003). S.D.Olabarriaga,M.Breeuwer,andW.J.Niessen,“Evaluation of Hessian-based filters to enhance the axis of coronary arteries
  50. (2002). scale in height ridge traversal for tubular object centerline extraction,”
  51. (1990). Scale-space and edge detection using anisotropic diffusion,”
  52. (1998). Scale-space derived from Bsplines,”
  53. (1983). Scale-space filtering,”
  54. Scale-space for discrete signals,”
  55. (1994). Scale-Space Theory in Computer Vision,K l u w e r Academic Publishers,
  56. (1994). Scale-space theory: a basic tool for analyzing structures at different scales,” J o u r n a lo fA p p l i e dS t a t i s t i c s ,
  57. (1996). Scale-space with casual time direction,”
  58. (2008). Scope of validity of PSNR in image/video quality assessment,”
  59. (2009). Segmentation of pulmonary vascular trees from thoracic 3D CT images,”
  60. (1992). Singularity detection and processing with wavelets,”
  61. (2009). Stallman and GCC Developer Community, Using the GnuCompilerCollection:AGnuManualforGCCVersion4.3.3,
  62. (2002). Statistical evaluation of image quality measures,”
  63. (2009). Steerable features for statistical 3D dendrite detection,”
  64. (2006). T h eD e fi n i t i v eG u i d et oG
  65. (1995). T.M.Koller,G.Gerig,G.Szekely,andD.Dettwiler,“Multiscale detectionofcurvilinearstructuresin2-Dand3-Dimagedata,” in
  66. ter Haar Romeny, Front-End Vision and Multi-Scale Image Analysis: Multi-scale Computer Vision Theory and Applications, Written
  67. The Debian System: Concepts and Techniques,
  68. (2010). The digital reconstruction of axonal and dendritic morphology (DIADEM
  69. (2001). The Gaussian derivative model for spatial-temporal vision: II.
  70. (2001). The influence of the gamma-parameter on feature detection with automatic scale Selection,”
  71. (1984). The structure of images,”
  72. (1993). The syntactical structure of scalar images,P h .
  73. (1980). Theory of edge detection,”
  74. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,”
  75. (1998). Threedimensional imaging of vasculature and parenchyma in intact rodent organs with X-ray micro-CT,”
  76. (2000). Tissue classification based on 3D local intensity structures for volume rendering,”
  77. (2006). Towards segmentation of irregular tubular structures in 3D confocal microscope images,”
  78. (1996). Tree structure and branching characteristics of the right coronary artery in a right-dominant human heart,”
  79. (1986). Uniqueness of the Gaussian kernel for scale-space filtering,” IEEETransactionsonPatternAnalysisandMachineIntelligence,
  80. Vessel Enhancing Diffusion Filter,”
  81. (2009). Vessel Enhancing Diffusion,”
  82. (2006). Vessel enhancing diffusion. A scale space representation of vessel structures,”MedicalImageAnalysis,vol.10,no.6,pp.815–825,
  83. (2003). W i t t ,C .H .R i e d e l ,M .G o e s s l ,M .S .C h m e l i k ,a n dE .L . Ritman, “Point spread function deconvolution in 3D microCT angiography for multiscale vascular tree separation,”
  84. (2001). Wavelet and scale-space theory in segmentation of airborne laser scanner data,”
  85. (2002). Why is image quality assessment so difficult?”
  86. (2001). Y o u n g ,R .M .L e s p e r a n c e ,a n dW .W .M e y e r ,“ T h e Gaussian derivative model for spatial-temporal vision: