Download Computational Methods and Clinical Applications for Spine by Jianhua Yao, Tobias Klinder, Shuo Li (eds.) PDF

By Jianhua Yao, Tobias Klinder, Shuo Li (eds.)

This booklet comprises the whole papers offered on the MICCAI 2013 workshop Computational equipment and scientific functions for backbone Imaging. The workshop introduced jointly researchers representing a number of fields, akin to Biomechanics, Engineering, drugs, arithmetic, Physics and Statistic. The works integrated during this publication current and speak about new traits in these fields, utilizing a number of equipment and methods which will handle extra successfully various and well timed functions related to sign and photograph acquisition, snapshot processing and research, photograph segmentation, photo registration and fusion, computing device simulation, picture dependent modelling, simulation and surgical making plans, picture guided robotic assisted surgical and snapshot established diagnosis.

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Read or Download Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan PDF

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Extra info for Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan

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Med. Imag. 22(5), 586–598 (2003) 8. : Automated modelbased vertebra detection, identification, and segmentation in ct images. Med. Image Anal. 13(3), 471–482 (2009) 9. : Introducing willmore flow into level set segmentation of spinal vertebrae. IEEE Trans. Biomed. Eng. 60(1), 115–122 (2013) 10. : Spine trauma. Radiol. Clin. N. Am. 49(1), 129–163 (2011) 11. : 3D statistical shape models for medical image segmentation. In: 3D Digital Imaging and Modeling, pp. 4–8 (1999) 12. : Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model.

After scaling the components of the ϑ f ∗ by A, the dissimilarity measure will be: r = SRϑ p − ϑ f ∗ (A) (15) and the squared magnitude of the above measure is summed over the image domain σ to get an optimization energy function: ⎞ ⎨ E ϑ p , ϑf ∗ = ⎟ ⎨ ⎞ Υε ϑp , ϑf ∗ r T rdσ , (16) σ where Υε is an indicator function defined as: Υε (ϑp , ϑf ∗ ) = 0 i f min (|ϑp |, |ϑf ∗ |) >ε , 1 i f min (|ϑp |, |ϑf ∗ |) ≤ ε (17) Due to Υε , all pixels of a distance (measured from the nearest point on the boundary) greater than ε are not considered in the energy optimization problem which reduces the computational time of our problem (Narrow-banding effect).

H. Lim et al. ture must be examined immediately. Image segmentation of spinal vertebrae in 3D allows detection, measurement, and monitoring of the fracture(s), and facilitates biomechanics analysis of the spinal column. Despite an increasing interest in spinal vertebrae segmentation in recent years, accurate 3D segmentation methods for diseased or fractured vertebrae are still lacking. There are some existing works in the literature for 2D or 3D segmentations, however they often require user intervention or fall short in achieving high accuracy [4, 6–8, 11–13, 15].

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