Research on a bifurcation location algorithm of a drainage tube based on 3D medical images

Fiorella D, Zuckerman SL, Khan IS, KumarN G, Mocco J (2015) Intracerebral hemorrhage: a common and devastating disease in need of better treatment. World Neurosurg 84(4):1136–1141. https://doi.org/10.1016/j.wneu.2015.05.063

Qureshi AI, Tuhrim S, Broderick JP, Batjer H, Hondo H, Hanley DF (2001) Spontaneous intracerebral hemorrhage. N Engl J Med 344(19):1450–1460. https://doi.org/10.1056/NEJM200105103441907

Xiao FR, Chiang IJ, Wong JM, Tsai YH, Huang KC, Liao CC (2011) Automatic measurement of midline shift on deformed brains using multiresolution binary level set method and Hough transform. Comput Biol Med 41(9):756–762. https://doi.org/10.1016/j.compbiomed.2011.06.011

Samadani U, Rohde V (2009) A review of stereotaxy and lysis for intracranial hemorrhage. Neurosurg Rev 32(1):15–22. https://doi.org/10.1007/s10143-008-0175-z

Wang GQ, Li SQ, Huang YH, Zhang WW, Ruan WW, Qin JZ et al (2014) Can minimally invasive puncture and drainage for hypertensive spontaneous basal ganglia intracerebral hemorrhage improve patient outcome: a prospective non-randomized comparative study. Mil Med Res 1:10. https://doi.org/10.1186/2054-9369-1-10

Zhou HG, Zhang Y, Liu L, Han X, Tao YH, Tang YP et al (2011) A prospective controlled study: minimally invasive stereotactic puncture therapy versus conventional craniotomy in the treatment of acute intracerebral hemorrhage. BMC Neurol 11:76. https://doi.org/10.1186/1471-2377-11-76

Delcourt C, Anderson C (2012) Acute intracerebral haemorrhage: grounds for optimism in management. J Clin Neurosci 19(12):1622–1626

Backlund EO, von Holst H (1978) Controlled subtotal evacuation of intracerebral haematomas by stereotactic technique. Surg Neurol 9(2):99–101. https://doi.org/10.1016/j.jocn.2012.05.018

Yan YF, Ru DW, Du JR, Shen X, Wang ES, Yao HB (2015) The clinical efficacy of neuronavigation-assisted minimally invasive operation on hypertensive basal ganglia hemorrhage. Eur Rev Med Pharmacol Sci 19(14):2614–2620

Cao YF (2019) Introduction of 3D slicer. https://www.slicercn.com/?page_id=485. Accessed 10 Apr 2019

Pinter C, Lasso A, Pieper S, Plesniak W, Kikinis R, Miller J (2019) Segment editor. https://slicer.readthedocs.io/en/latest/user_guide/module_segmenteditor.html. Accessed 10 Apr 2019

Zhang XL, Zhang KX, Pan QL, Chang JC (2019) Three-dimensional reconstruction of medical images based on 3D slicer. J Complexity Health Sci 2(1):1–12. https://doi.org/10.21595/chs.2019.20724

Pszczolkowski S, Law ZK, Gallagher RG, Meng DW, Swienton DJ, Morgan PS et al (2019) Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage. Comput Biol Med 106:126–139. https://doi.org/10.1016/j.compbiomed 2019.01.022

Zhang J, Yan CH, Chui CK, Ong SH (2010) Fast segmentation of bone in CT images using 3D adaptive thresholding. Comput Biol Med 40(2):231–236. https://doi.org/10.1016/j.compbiomed.2009.11.020

Wagstaff K, Cardie C, Rogers S, Schrodl S (2001) Constrained k-means clustering with background knowledge. In: Abstracts of the 18th international conference on machine learning. Morgan Kanufman Press, San Francisco

Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36(2):451–461. https://doi.org/10.1016/S0031-3203(02)00060-2

Bai QH (2010) Analysis of particle swarm optimization algorithm. Computer Inf Sci 3(1):180–184. https://doi.org/10.5539/cis.v3n1p180

Xing H, Pan XJ (2018) Application of improved particle swarm optimization in system identification. In: Abstracts of 2018 Chinese control and decision conference. IEEE, Shenyang. https://doi.org/10.1109/CCDC.2018.8407336

Su Q, Yang LH, Fu YG, Wu YJ, Gong XT (2014) Parameter training approach based on variable particle swarm optimization for belief rule base. J Comput Appl 34(8):2161–2165

Khong SZ, Nešić D, Manzie C, Tan Y (2013) Multidimensional global extremum seeking via the DIRECT optimisation algorithm. Automatica 49(7):1970–1978. https://doi.org/10.1016/j.automatica.2013.04.006

Cui JZ, Cui Y (2019) The multifunctional drainage tube with multi-tube for intracranial hematoma. CN patent CN208405725U

Siddiqi K, Pizer SM (2008) Medial representations: mathematics, algorithms and applications. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8658-8

Wang Y, Li J, Chen S (2011) A novel method of extracting 3D blood vessel images axis based on energy constraint equation. J Comput Inf Syst 7(4):1319–1327

Zhong YJ, Chen FL (2018) Computing medial axis transformations of 2D point clouds. Graph Model 97:50–63. https://doi.org/10.1016/j.gmod.2018.03.004

Zhong YJ (2018) Computing medial axis transformations of the geometric model. J Comput Aided Des Comput Graph 30(8):1394–1412. https://doi.org/10.3724/SP.J.1089.2018.16790

Feng CS, Cong S, Feng XY (2007) A new adaptive inertia weight strategy in particle swarm optimization. In: Abstracts of 2007 IEEE congress on evolutionary computation. IEEE, Singapore, pp 25–28. https://doi.org/10.1109/CEC.2007.4425017

Zhang LM, Tang YG, Hua CC, Guan XP (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl Soft Comput 28:138–149. https://doi.org/10.1016/j.asoc.2014.11.018

Li ZQ, Zheng H, Pei CM (2010) Particle swarm optimization algorithm based on adaptive inertia weight. In: Abstracts of the 2010 2nd international conference on signal processing systems. IEEE, Dalian, pp 5–7

Ao YC, Shi YB, Zhang W, Li YJ (2014) Improved particle swarm optimization with adaptive inertia weight. J Univ Electron Sci Technol China 43(6):874–880

Li LS, Zhang XJ (2018) New chaos particle swarm optimization based on adaptive inertia weight. Comput Eng Appl 54(9):139–144