Computer-aided hip surgery planning and implant design applications require accurate segmentation of
femoral head and proximal acetabulum. An accurate outer surface extraction of femoral head using marching cubes
algorithm remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we
present an automatic and fast approach for segmentation of femoral head and proximal acetabulum which leads to
accurate and compact representation of femoral head using marching cubes algorithm. At first, valley-emphasized
images are constructed from original images so that valleys stand out in high relief. Otsu’s multiple thresholding
technique is applied to seperate the images into bone and non-bone classes. Region growing method and threedimensional
(3D) morphological operations are performed to fill holes in the bone. In the reclassification process,
the bone regions are further segmented, and the boundaries of the bone regions are further refined based on
Bayes decision rule. Finally, marching cubes algorithm is applied to reconstruct a 3D model and extract the outer
surface of femoral head and proximal acetabulum. Experimental results show that this method is an accurate
segmentation technique for femoral head and proximal acetabulum and it can be applied as a tool in medical
practice.
WAN Daqian1 (万大千), WANG Dong2 (王东), MA Anbang4 (马安邦),DAI Kerong2,4 (戴尅戎), AI Songtao3* (艾松涛), WANG Liao2* (王燎)
. Automatic Outer Surface Extraction of Femoral Head in CT Images[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(3)
: 377
-384
.
DOI: 10.1007/s12204-017-1847-z
[1] SUGANO N. Computer-assisted orthopedic surgery[J]. Journal of Orthopaedic Science, 2003, 8(3): 442-448.
[2] VICECONTI M, ZANNONI C, TESTI D, et al. CTdata sets surface extraction for biomechanical modelingof long bones [J]. Computer Methods and Programsin Biomedicine, 1999, 59(3): 159-166.
[3] ZOROOFI R A, SATO Y, NISHII T, et al. Automatedsegmentation of necrotic femoral head from 3D MRdata [J]. Computerized Medical Imaging & Graphics,2004, 28(5): 267-278.
[4] GANZ R, GILL T J, GAUTIER E, et al. Surgical dislocationof the adult hip: A technique with full accessto the femoral head and acetabulum without the riskof avascular necrosis [J]. The Bone & Joint Journal,2001, 83(8): 1119-1124.
[5] CHENG Y Z, ZHOU S J, WANG Y D, et al. Automaticsegmentation technique for acetabulum andfemoral head in CT images [J]. Pattern Recognition,2013, 46(11): 2969-2984.
[6] HILDEBRAND T, LAIB A, M¨ULLER R, et al. Directthree-dimensional morphometric analysis of humancancellous bone: Microstructural data from spine, femur,iliac crest, and calcaneus [J]. Journal of Bone &Mineral Research, 1999, 14(7): 1167-1174.
[7] MCINERNEY T, TERZOPOULOS D. A dynamic finiteelement surface model for segmentation and trackingin multidimensional medical images with applicationto cardiac 4D image analysis [J]. Journal of ComputerizedMedical Imaging and Graphics, 1995, 19(1):69-83.
[8] KANG Y, ENGELKE K, KALENDER W A. A newaccurate and precise 3-D segmentation method forskeletal structures in volumetric CT data. [J]. IEEETransactions on Medical Imaging, 2003, 22(5): 586-598.
[9] KAINMUELLER D, LAMECKER H, ZACHOW S, etal. An articulated statistical shape model for accuratehip joint segmentation [C]//Engineering in Medicineand Biology Society, 2009 (EMBC 2009): InternationalConference of the IEEE. [s.l.]: IEEE, 2009:6345-6351.
[10] COMTAT C, KINAHAN P E, DEFRISE M, et al. Fastreconstruction of 3D PET data with accurate statisticalmodeling [J]. IEEE Transactions on Nuclear Science,1998, 45(3): 1083-1089.
[11] MISHRA A K, FIEGUTH PW, CLAUSID A.Decoupledactive contour (DAC) for boundary detection [J].IEEE Transactions on Pattern Analysis and MachineIntelligence, 2011, 33(2): 310-324.
[12] XU M H, THOMPSON P M, TOGA A W. An adaptivelevel set segmentation on a triangulated mesh [J].IEEE Transactions on Medical Imaging, 2004, 23(2):191-201.
[13] KASS M, WITKIN A, TERZOPOULOS D. Snakes:Active contour models [J]. International Journal ofComputer Vision, 1988, 1(4): 321-331.
[14] HAAS B, CORADI T, SCHOLZ M, et al. Automaticsegmentation of thoracic and pelvic CT images for radiotherapyplanning using implicit anatomic knowledgeand organ-specific segmentation strategies [J].Physics in Medicine and Biology, 2008, 53(6): 1751-1771.
[15] CHICA A, MONCL′US E, BRUNET P, et al. Exampleguidedsegmentation [J]. Graphical Models, 2012,74(6), 302-310.
[16] BIENIEK A, MOGA A. An efficient watershed algorithmbased on connected components [J]. PatternRecognition, 2000, 33(6): 907-916.
[17] KIM Y, KIM D. A fully automatic vertebra segmentationmethod using 3D deformable fences [J]. ComputerizedMedical Imaging and Graphics, 2009, 33(5):343-352.
[18] ZOROOFI R A, SATO Y, SASAMA T, et al. Automatedsegmentation of acetabulum and femoral headfrom 3-D CT images [J]. IEEE Transactions on InformationTechnology in Biomedicine, 2003, 7(4): 329-343.
[19] DROOGENBROECK M V, TALBOT H. Fast computationof morphological operations with arbitrarystructuring elements [J]. Pattern Recognition Letters,1996, 17(14): 1451-1460.
[20] HUANG Y, WANG S. Multilevel thresholding methodsfor image segmentation with Otsu based on QPSO[C]//2008 Congress on Image and Signal Processing.[s.l.]: IEEE Computer Society, 2008: 701-705.
[21] ZHANG Y D,WU L N. Optimal multi-level thresholdingbased on maximum tsallis entropy via an artificialbee colony approach [J]. Entropy, 2011, 13(4): 841-859.
[22] LIU Y, ZHAO Y L. Quick approach of multi-thresholdOtsu method for image segmentation [J]. Journal ofComputer Applications, 2011, 31(12): 3363-3365 (inChinese).
[23] ADAMS R, BISCHOF L. Seeded region growing [J].IEEE Transactions on Pattern Analysis and MachineIntelligence, 1994, 16(6): 641-647.
[24] DEHMESHKI J, AMIN H, VALDIVIESO M, et al.Segmentation of pulmonary nodules in thoracic CTscans: A region growing approach [J]. IEEE Transactionson Medical Imaging, 2008, 27(4): 467-480.
[25] KUMARI V V, SURIYANARAYANAN N. Blood vesselextraction using Wiener filter and morphologicaloperation [J]. International Journal of Computer Scienceand Emerging Technology, 2010, 1(4): 7-10.
[26] SERLIE I, TRUYEN R, FLORIE J, et al. Computedcleansing for virtual colonoscopy using a threematerialtransition model [C]// Medical Image Computingand Computer-Assisted Intervention: MICCAI2003. Berlin Heidelberg: Springer, 2003: 175-183.
[27] POHL K M, FISHER J, GRIMSON W E, et al. ABayesian model for joint segmentation and registration[J]. Neuroimage, 2006, 31(1): 228-239.
[28] QI Y Y, XIONG W, LEOW W K, et al. Semiautomaticsegmentation of liver tumors from CT scansusing bayesian rule-based 3D region growing [EB/OL].http://www.comp.nus.edu.sg/~leowwk/papers/miccai2008-tumor1.pdf
[29] LORENSEN W E, CLINE H E. Marching cubes: Ahigh resolution 3D surface construction algorithm [J].ACM SIGGRAPHY Computer Graphics, 1987, 21(4):163-169.
[30] GELAUDE F, VANDER SLOTEN J, LAUWERS B.Accuracy assessment of CT-based outer surface femurmeshes [J]. Computer Aided Surgery, 2008, 13(4): 188-199.