CFP last date
22 April 2024
Reseach Article

A Hierarchical Registration Algorithm based on Comparative Centerline Assessment Technique for Evaluating Altered Tortuosity in Cerebral Vessels

by Abanti Shama Afroz, Puspita Saha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 10
Year of Publication: 2015
Authors: Abanti Shama Afroz, Puspita Saha
10.5120/ijca2015907010

Abanti Shama Afroz, Puspita Saha . A Hierarchical Registration Algorithm based on Comparative Centerline Assessment Technique for Evaluating Altered Tortuosity in Cerebral Vessels. International Journal of Computer Applications. 129, 10 ( November 2015), 24-29. DOI=10.5120/ijca2015907010

@article{ 10.5120/ijca2015907010,
author = { Abanti Shama Afroz, Puspita Saha },
title = { A Hierarchical Registration Algorithm based on Comparative Centerline Assessment Technique for Evaluating Altered Tortuosity in Cerebral Vessels },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 10 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number10/23110-2015907010/ },
doi = { 10.5120/ijca2015907010 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:03.304518+05:30
%A Abanti Shama Afroz
%A Puspita Saha
%T A Hierarchical Registration Algorithm based on Comparative Centerline Assessment Technique for Evaluating Altered Tortuosity in Cerebral Vessels
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 10
%P 24-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Three-dimensional cerebral registration is mostly performed with atlas based methods. However, in case of compact and pre-constructed (segmented) regions of interest (ROIs) involving only blood vessels which are prone to torturous changes; atlas-based approach does not offer the best output. This article suggests a hierarchical (top to bottom) skeleton based registration approach for similar cases. The method has been applied on five sets of cerebral artery locations with aneurysms in order to evaluate their post invasive structural changes. The algorithm works in a semi-automatic manner where the bifurcation zone has been selected as the reference zone. This landmark matching approach works as the basis of the initial stage, coarse affine transformation. The non-rigid intermediate stage is optional and is dependent on the difference of the comparative angular orientation of the models in three dimensional space. Afterward, a third stage of iterative affine transformation is applied for finer adjustments if there is scope for any. Once registered with limiting boundaries, the branch by branch structural comparisons are interpreted quantitatively with box and whisker plots. In order to verify the proposed method, overlapping for one of the fifteen branch sets has also been evaluated with dice similarity indices. The resulting comparison gives a good support in favour of the proposed method.

References
  1. Ma, Z. et al., 2010. A Review of Algorithms for Medical Image Segmentation and their Applications to the Female Pelvic Cavity A Review of Algorithms for Medical Image Segmentation and their Applications to the Female Pelvic Cavity. Computer methods of biomechanical and biomedical engineering, 13(2), pp.235–246.
  2. Pham, D.L., Xu, C. & Prince, J.L., 2000. A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering, 2, pp.315–337.
  3. Afroz, A.S., An Alternative Approach of Evaluating Dice Similarity Index for Pre-segmented Blood Vessels. In Software, Knowledge, Information Management and Applications (SKIMA), 2014 8th International Conference on. IEEE, 2014. Dhaka, pp. 4–7.
  4. Hilsmann, A. et al., 2007. Deformable 4DCT lung registration with vessel bifurcations. In In International Conference of Computer Assisted Radiology and Surgery (CARS).
  5. Matsopoulos, G.K. et al., Multimodal registration of retinal images using self organizing maps. Medical Imaging, IEEE Transactions, 23(12), pp.1557–1563.
  6. Hsu, C.-Y. et al., 2015. Medical Image Processing for Fully Integrated Subject Specific Whole Brain Mesh Generation. Technologies, 3(2), pp.126–141. Available at: http://www.mdpi.com/2227-7080/3/2/126/ [Accessed October 13, 2015].
  7. Wang, F. & Baba C. Vemuri., 2005. Simultaneous registration and segmentation of anatomical structures from brain MRI. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2005., pp.17–25.
  8. Prasad, G., 2013. Brain Mapping Methods: Segmentation, Registration, and Connectivity Analysis.
  9. Han, X. & Fischl, B., 2007. Atlas renormalization for improved brain MR image segmentation across scanner platforms. Medical Imaging, IEEE Transactions, 26(4), pp.479–486.
  10. Khan, A.R., Chung, M.K. & Beg, M.F., 2009. Robust atlas-based brain segmentation using multi-structure confidence-weighted registration. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009, pp.549–557.
  11. Antiga, L., 2002. Patient-specific modeling of geometry and blood flow in large arteries. Politecnico di Milano.
  12. De Bock, S. et al., 2012. Our capricious vessels: The influence of stent design and vessel geometry on the mechanics of intracranial aneurysm stent.
  13. Ma, D. et al., 2011. FINITE ELEMENT STUDY OF CONFORMITY OF FLOW DIVERTER WITH. In ASME 2011 Summer Bioengineering Conference SBC2011. pp. 1–2.
  14. Anon, Sørensen–Dice coefficient. Available at: http://en.wikipedia.org/wiki/Sorensen?Dice_coefficient [Accessed September 19, 2014].
Index Terms

Computer Science
Information Sciences

Keywords

Pre-segmented neural blood vessels centerline matching auto landmark identification