Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique
C. Priyanka1 , M. Vanitha2 , S. Anitha3
Section:Research Paper, Product Type: Journal Paper
Volume-8 ,
Issue-2 , Page no. 26-30, Feb-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i2.2630
Online published on Feb 28, 2020
Copyright © C. Priyanka, M. Vanitha, S. Anitha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: C. Priyanka, M. Vanitha, S. Anitha, “Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.26-30, 2020.
MLA Style Citation: C. Priyanka, M. Vanitha, S. Anitha "Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique." International Journal of Computer Sciences and Engineering 8.2 (2020): 26-30.
APA Style Citation: C. Priyanka, M. Vanitha, S. Anitha, (2020). Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique. International Journal of Computer Sciences and Engineering, 8(2), 26-30.
BibTex Style Citation:
@article{Priyanka_2020,
author = {C. Priyanka, M. Vanitha, S. Anitha},
title = {Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2020},
volume = {8},
Issue = {2},
month = {2},
year = {2020},
issn = {2347-2693},
pages = {26-30},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5025},
doi = {https://doi.org/10.26438/ijcse/v8i2.2630}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i2.2630}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5025
TI - Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique
T2 - International Journal of Computer Sciences and Engineering
AU - C. Priyanka, M. Vanitha, S. Anitha
PY - 2020
DA - 2020/02/28
PB - IJCSE, Indore, INDIA
SP - 26-30
IS - 2
VL - 8
SN - 2347-2693
ER -
VIEWS | XML | |
319 | 342 downloads | 149 downloads |
Abstract
Intravascular Ultrasound (IVUS) is a surgical representational process which used to see the plasma vessels out through the conterminous blood column by blood vessels in persons to determine the amount of accretion of degenerative substantial built up at in the pericardial coronary vein which cannot be envisaged by Angiography. It harvests the vessel fractious sectional images of plasma vessels that provide the measureable and qualitative valuation of the vascular wall info about the nature of atherosclerosis abrasions as well as plaque size and shape. The credentials of lumen, media and adventitia restrictions in IVUS imaginings is essential for an effectual assessment of the atherosclerotic commemorations. During an IVUS inspection, a catheter with an ultrasound transducer is announced in the physique through a plasma container and then dragged back to appearance sequence of container cross sections. This paper accessible a one of the good-looking and collaborating methods is the Active Curve Prototypical Method (ACM) with Limited Image Fit Dynamism Minimization (LIFEM) method which has been widely used in medical imaging performance as it always produces computationally well-organized for sub-regions with incessant boundaries. In our approach preserves and deals with the boundary regularization property and sub-pixel exactitude.
Key-Words / Index Term
Intravascular ultrasound (IVUS), Vessel Fractious Sectional Images, Credentials of Lumen, Active Curve Prototypical Method (ACM) with Limited Image Fit Dynamism Minimization (LIFEM), Boundary Regularization
References
[1] Alison Noble,
Djamal Boukerroui. “Ultrasound image
segmentation a survey”, IEEE Transactions on Medical Imaging, Institute of
Electrical and Electronics Engineers, 25(28),
pp.987-1010, 2006.
[2] Michael Kass, Andrew Witkin, and Demetri Terzopoulos, “Snakes Active contour models”,International
Journal of Computer Vision, pp.321-331, 1988.
[3] Tony F.Chan, Luminita A.Vese,” Active Contour without edges”, IEEE Transactions on image
Processing, 10(2), pp.266-277, 2001.
[4] M.Sonka,
Xiangmin Zhang, M.Seibes, M.S.Bissing, S.C.DeJong, S.M.Collins, C.R.McKay, “Segmentation of intravascular ultrasound
images a knowledge-based approach”,IEEE Transactions on Medical Imaging, 14 (4), pp.719-73, 1995,
[5]
J.D.Klingensmith, R.Shekhar, D.G.Vince, “Evaluation of three-dimensional
segmentation algorithms for the identification of luminaland
medical-adventitial borders in intravascular ultrasound images”, IEEE
Transactions on Medical Imaging, 19(10),
pp.996-1011, 2000.
[6]
George D.Giannoglou, Yiannis Chatzizisis, Vassilis
Koutkias, Ioannis Kompatsiaris, Maria Papadogiorgaki, Vasileios Mezaris, Eirini
Parissi, Panagiotis Diamantopoulos, Michael G.Strintzis NicosMaglaveras,
George E.Parcharidis, George E.Louridas, “A
novel active contour model for fully automated segmentation of intravascular
ultrasound images in vivo validation in human coronary arteries”, Computers in Biology and Medicine, 37(9),pp.1292-1302. 2007.
[7] Chenyang Xu,Jerry L.Prince, “Snakes, Shapes, and gradient Vector flow”, IEEE Transactions
on Image processing, 7(3), pp.359-369, 1998.
[8] HassenLazrag, KamelAloui, Med Saber Naceur, “Automatic Segmentation of Lumen in
Intravascular Ultrasound Images Using Fuzzy Clustering and Active Contours”,
International Conference on Control, engineering & Information Technology
(CEIT’13) Proceedings Engineering & Technology, Vol.1, pp.58-63, 2013.
[9] Elisabeth Brusseau, Chris L.de Korte, Frits Mastik,
Johannes Schaar, and Anton F.W.Van der Steen, “Fully Automatic Luminal Contour Segmentation in Intracoronary
Ultrasound Imaging A Statistical
Approach”, IEEE Transactions on Medical imaging, 23(5), pp.554-566, 2004.
[10] Oddvar Husby and Havard Rue, “Estimating blood vessel areas in ultrasound images using a deformable
template model”, StatisticalModelling,
4(3), pp.211-226, 2004.
[11] P.Martin,P.Refregier, F.Goudail, F.Guerault, “Influence of the noise model on level set
active contour segmentation”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, 26(6), pp.799-803, 2004.
[12] AndrewHammoude,
“Endocardial border Identification in
two-dimensional echocardiographic images, Review of Methods”,Computerized
Medical Imaging and Graphics, 22(3), pp.181-193, 1998.
[13] E. Brusseau, C.L. de Korte, F. Mastik, J. Schaar, A.F.
van der Steen, “Fully automatic luminal
contour segmentation in intracoronary ultrasound imaging”-A statistical
approach, IEEE Trans. Med. Imaging 23,
554–566, 2004.
[14] J.D. Klingensmith, R. Shekhar, D.G. Vince, “Evaluation of threedimensional segmentation
algorithms for the identification of luminal and medial-adventitial borders in
intravascular ultrasound images”, IEEE Trans. Med. Imaging 19, 996–1011, 2000.
[15] G.Sowmiya, V.Kumutha, “Facial Expression Recognition
Using Static Facial Images ”, International journal of Scientific Research in
Computer Science and Engineering , Vol.6, Issue.2, pp.72-75,2018.
Ratnesh Kumar Shukla, Ajay
Agarwal, Anil Kumar Malviya, “An Introduction of Face Recognition and Face
Detection for Blurred and Noisy Images”, International journal of
Scientific Research in Computer Science and Engineering, Vol.6,
Issue.3, pp.39-43,2018.