IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Automatic Coronary Centerline Extraction From Computed Tomography Images

[Full Text]

 

AUTHOR(S)

K.SUDHAKAR, S.ARUNPRATHAP

 

KEYWORDS

Coronary Centerline Extraction, Gradient Vector Flow (GVF), Fast Marching method, Computed Tomography

 

ABSTRACT

The cardiac attack disease is increased in a decade of years. Image processing technique plays a vital role in identifying coronary artery diseases. The coronary artery disease reduces the flow of blood to the heart. The techniques used to identify the problem in heart should be fast and accurate. This paper builds up another system for separating coronary centerlines from three dimensional fragmented coronary veins models. In the proposed system we apply the Gradient Vector Flow algorithm for the vessel model of speed image. The centerline in the heart can be extracted by using the wave propagation method. The three dimensional vessel model has been implemented in many coronary vein segment. The coronary centerline extraction in artificial vessel model is adapted with our proposed framework. The distance to extract the coronary centerline is about 0.25mm and the overlap distance is about 96%. The quality of Computed Tomography image is 0.3mm × 0.3mm × 0.4mm. Next the artificial vessel model will goes to further testing process. The CT scan image is valid for both left coronary artery and right coronary artery. The average time to take examine the arteries is about 16 min per case. The Gradient vector flow method and fast marching method may be suitable for all cardiac patients and this technique will be more accurate and processing time is fast.

 

REFERENCES

[1] J. R. Swedlow, I. Goldberg, E. Brauner, and P. K. Sorger, ``Informatics and quantitative analysis in biological imaging,'' Science, vol. 300, no. 5616,pp. 100_102, 2003.
[2] B. Bouraoui, C. Ronse, J. Baruthio, N. Passat, and P. Germain, ``Fully automatic 3D segmentation of coronary arteries based on mathematical
[3] morphology,'' in Proc. 5th IEEE Int. Symp. Biomed. Imag., Nano Macro, 2008, pp. 1059_1062.
[4] H. Tek, M. A. Gülsün, S. Laguitton, L. Grady, D. Lesage, and G. Funka- Lea, ``Automatic coronary tree modeling,'' Insight J., pp. 1_8, Aug. 2008.
[5] C. Wang and O. Smedby, ``An automatic seeding method for coronaryartery segmentation and skeletonization in CTA,'' Insight J., pp. 1_8, 2008.
[6] N. D. Cornea, D. Silver, and P. Min, ``Curve-skeleton applications,'' inProc. IEEE Vis., Oct. 2005, pp. 95_102.
[7] S. Ukil and J. M. Reinhardt, ``Smoothing lung segmentation surfaces in three-dimensional X-ray CT images using anatomic guidance,'' Acad.Radiol., vol. 12, no. 12, pp. 1502_1511, 2005.
[8] R. Van Uitert and I. Bitter, ``Subvoxel precise skeletons of volumetric databased on fast marching methods,'' Med. Phys. vol. 34, no. 2, pp. 627_638,2007.
[9] H. Cui et al., ``Fast marching and Runge_Kutta based method for centerline extraction of right coronary artery in human patients,'' Cardiovascular Eng. Technol., vol. 7, no. 2, pp. 159_169, 2016.
[10] M. S. Hassouna and A. A. Farag, ``Variational curve skeletons using gradient vector _ow,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. 31,no. 12, pp. 2257_2274, Dec. 2009.
[11] P. H. Kitslaar, J. Dijkstra, B. Stoel, J. H. C. Reiber, M. Frenay, and E. Oost, ``Connected component and morpholgy based extraction of arterial centerlines of the heart,'' Midas J., pp. 1_8, Aug. 2008.
[12] R. Manniesing, M. A. Viergever, and W. J. Niessen, ``Vessel enhancing diffusion: A scale space representation of vessel structures,'' Med. Image Anal., vol. 10, no. 6, pp. 815_825, 2006.
[13] S. Arunprathap and K. Sudhakar “High Efficient Modified Vertically Grown Glucose Sensor On Electrode Based Zro2/Tio2 Nano Particles” Bioscience Biotechnology Research Communications SPECIAL ISSUE 11 NUMBER-2 (2018)
[14] K. Sudhakar and S. Arunprathap “The Realtime Multiparameter Based Patient Monitoring System” Bioscience Biotechnology Research Communications SPECIAL ISSUE 11 NUMBER-2 (2018)