Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique
DOI:
https://doi.org/10.26438/ijcse/v8i2.2630Keywords:
Intravascular ultrasound (IVUS, Vessel Fractious Sectional Images, Credentials of Lumen, Active Curve Prototypical Method (ACM) with Limited Image Fit Dynamism Minimization (LIFEM), Boundary RegularizationAbstract
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.
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”, IEEETransactions 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.
[16] 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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
