Inevitable dissection of left ventricle aimed at discovery of cardiac blood flow appraisal

  • Authors

    • Inbalatha M.
    • Kalaivani S.
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.5.9138
  • Bland-Altman Analysis, Circular Hough Transform, Dissection, End Diastolic Volume, and MRI.

  • Abstract

    Cardiac Magnetic Resonance Images (MRI) are chastely indispensable implement for truthful demarcation of the left Ventricle myocardium for purposeful and serviceable cardiac examination. Dissection of  MRI principally the leftward ventricle (LV) is a blistering focus with extraordinary perplexing owed to deceive delineation such as percentages misalignment in its position, in homogeneity of sequential portions and to clinch divergence in focus of adjoining pixels for the change in the body fluid drift. In this effort, we put forward a scheme to inevitably fragment the leftward Ventricle of  MRI images by Round Hough Transform (RHT) initially by locating the seed point stalked a circle with apt radius as basal region and verdict the ROI  in the image. Adjoining the center, apex region of the left ventricle is positioned till the extent and shape connection obsolete plus the mid region from the intermediate ventricle to the base up to the myocardial fringe. This same images of 21 subjects are compared with the manual delineation method by locating the center manually.  Mean and Standard deviation is calculated for the basal, mid and apex regions of the LV for auto and manual segmentation. Bland –Altman investigation is performed for End Diastolic Measurements in volume (EDV) with the existing investigations and found to be more close to the ground truth results and superior than the previously enduring methods. To conclude the projected segmentation process truthfully locates and segments the LV which trials the one hundredth of blood pour. EDV measurements are established with less computational time to find the patient’s heart failure with myocardial Infarction.

    Objective: To spot the cardiac maladies triggered owed to stumbling block of blood by dissection of left Ventricle with MRI image.

  • References

    1. [1] A.Frangi, W. Niessen, and M.A. Viergever, “ThreeDimensional Modeling for Functional Analysis of Cardiac Images: A Reviewâ€, TMI, IEEE, 2001, 20(1), pp. 2–25.

      [2] Lijia Wang, Mengchao Pei, Noel C.F.Codella, Minisha Kochar, Jonathan. W. Weinsaft, JianqiLi, Martin R. Prince, and Yi Wang “Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)’’, HPC, BioMed Research International Vol. 2015, Article ID: 367583, pp. 1-9.

      [3] K. S. L. Teo, A. Carbone, C. Piantadosi et al., “Cardiac MRI assessment of left and right ventricular parameters in healthy Australian normal Volunteers,â€Heart Lung and Circulation, vol. 17, no.4, pp.313–317, 2008.

      [4] L.Bonnemains, D.Mandry, P.-Y.Marie,E.Micard,B.Chen, and P.-A.Vuissoz,“Assessment of right ventricle volumes and function by cardiac MRI: Quantification of the regional and global inter observer variability,â€Magnetic Resonance in Medicine, vol. 67, no.6, pp.1740–1746, 2012.

      [5] S. H. James, R. Wald, B. J. Wintersperger et al., “Accuracy of right and left ventricular functional assessment by short-axis vs axial cine steady-state Free-precession magnetic resonance imaging: intrapatient correlation with main pulmonary artery and ascending aorta phase-Contrast flow measurements,â€Canadian Association of Radiologists Journal, vol.64, no.3, pp.213– 219, 2013.

      [6] A.la Gerche, G. Claessen, A. van de Bruaene et al., “Cardiac MRI: a new gold standard for ventricular volume quantification during high-Intensity exercise,â€Circulation: Cardiovascular Imaging, vol.6, no.2, pp.329–338, 2013.

      [7] J.Caudron, J.Fares, V.Lefebvre, P.-H.Vivier, C.Petitjean, andJ.N.Dacher, “Cardiac MRI assessment of right ventricular function in acquired heart disease: factors of variability,†Academic Radiology,vol.19,no.8,pp.991–1002,2012.

      [8] A.Singh, L. Kurowski, and M. Y. Chiu, “Cardiac MR image segmentation using deformable modelsâ€, In Proc. SPIE BMIPBMV., San Jose, USA, 1993, vol.1905, pp. 8–28.

      [9] Jia Liang, Gangyi Ding, Yuwei Wu, “Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake†, DOI:10.1109/BMEI.2008.188, IEEE, pp. 238 -242, 2008.

      [10] T. Fujino et al., “New method of on-line quantification of regional wall motion with automated segmental motion analysis,â€J. Am. Soc.Echocardiograph, Elsevier, 2001, 14(9), pp. 892–901.

      [11] J. Feng, W. Lin, C. Chen, “Epicardial boundary detection using fuzzy reasoningâ€, TMI.,IEEE,1991,10 (2),pp.187–199.

      [12] A.Lalande, M.Jaulent, “A fuzzy automaton to detect and quantify artery lesions from arteriogramsâ€, In Proc. IPMU., Granada,

      [13] Spain, 1996, pp.1481–1487.

      [14] H. G. Bosch, S. C. Mitchell, B. P. F. Lelieveldt, F. Nijland, O. Kamp, M. Sonka, and J. H. C. Reiber, “Active appearance-motion models for fully Automated endocardial contour detection in time sequences of echocardiograms,†In Proc.CARS. Elsevier, Amsterdam, Netherlands, 2001, vol.1230, pp.941-947.

      [15] T. F. Cootes, G. J. Gareth, and C. J. Taylor, “Active appearance models,†TPAMI.,IEEE,2001,23(6),pp.681–685.

      [16] M. Kass and A. Witkin, D. Terzo poulos, “Snake: active contour modelsâ€, IJCV, Kluwer, 1988, 1(4), pp.321-331.

      [17] S. Ranganth, “Contour extraction from cardiac MRI Studies using Snakesâ€, TMI, IEEE, 1995, 14(2), pp.328-338.

      [18] C. Xu, J.L. Prince, “Gradient vector flow: A new external force for snakesâ€, In Proc.CVPR., IEEE, San Juan, Puerto Rico,1997, pp. 66-71.

      [19] C. Xu,J. L. Prince, “Snakes, shapes, and gradient vector flowâ€, TIP, IEEE, 1998, pp.359-369.

      [20] S. Osher, J. Sethiam, “Fronts propagating with curvature –dependent speed: Algorithms based on Hamilton-Jacobi formulation,†J. Computational Phys., Elsevier, 1988, pp. 12–49.

      [21] D. Mendoza, N. Codella, Y. Wang et al., “Impact of diastolic dysfunction severity on global left ventricular volumetric filling—assessment by automated segmentation of routine cine cardiovascular magnetic resonance,†Journal of Cardiovascular Magnetic Resonance, vol.12, no.1, article 46, 2010.

      [22] B. Grac¸a, P. Donato, M. J. Ferreira, M. Castelo-Branco, and F. Caseiro-Alves, “Left ventricular diastolic function in type 2 diabetes mellitus and the association with coronary artery calcium score: a cardiac MRI study,†The American Journal of Roentgenology, vol.202, no.6, pp.1207–1214, 2014.

      [23] N. C. F. Codella, M. D. Cham, R.Wong et al., “Rapid and accurate left ventricular chamber quantification using a novel CMR segmentation algorithm: a clinical validation study,†Journal of Magnetic Resonance Imaging, vol. 31, no. 4, pp. 845–853, 2010.

      [24] E. Heiberg, J. Sj¨ogren, M. Ugander, M. Carlsson, H. Engblom, and H. Arheden, “Design and validation of segment—freely available software for cardiovascular image analysis,†BMC Medical Imaging, vol. 10, article 1, 2010.

      [25] [24] G.Messalli, A. Palumbo, E.Maffei et al., “Assessment of left ventricular volumes with cardiac MRI: comparison between two semi-automated quantitative software packages,†La Radiologia Medica, vol. 114, no. 5, pp. 718–727, 2009.

      [26] C. Petitjean and J.-N. Dacher, “A review of segmentation methods in short axis cardiac MR images,†Medical Image Analysis, vol. 15, no. 2, pp. 169–184, 2011.

      [27] R. S. Inamdar andD. S. Ramdasi, “Active appearance models for segmentation of cardiac MRI data,†in Proceedings of the International Conference on Communications and Signal Processing (ICCSP ’13), pp. 96–100, 2013.

      [28] H.Hu, H. Liu, Z. Gao, and L.Huang, “Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming,†Magnetic Resonance Imaging, vol. 31, no. 4, pp. 575–584, 2013.

      [29] Eslami, A. Karamalis, A. Katouzian, and N.Navab, “Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI,†Medical Image Analysis, vol. 17, no. 2, pp. 236–253, 2013.

      [30] X. Yang, Y. Su, M. Wan et al., “Left ventricle segmentation by dynamic shape constrained random walks,†in Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '14), pp. 4723–4726, Chicago, Ill, USA, August 2014

      [31] S. P. Dakua, “AnnularCut: a graph-cut design for left ventricle segmentation from magnetic resonance images,†IET Image Processing, vol. 8, no. 1, pp. 1–11, 2014.

      [32] D. Grosgeorge, C. Petitjean, and S. Ruan, “Joint segmentation of right and left cardiac ventricles using multi-label graph cut,†in Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI ’14), pp. 429–432, Beijing, China, April-May 2014.

      [33] D. Grosgeorge, C. Petitjean, J.-N. Dacher, and S. Ruan, “Graph cut segmentation with a statistical shape model in cardiac MRI,†Computer Vision and Image Understanding, vol. 117, no. 9, pp. 1027–1035, 2013.

      [34] Y. Liu, C. Li, S. Guo, Y. Song, and Y. Zhao, “A novel level set method for segmentation of left and right ventricles from cardiac MR images,†in Proceedings of the 36thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ’14), pp. 4719–4722, Chicago, Ill, USA, August 2014.

      [35] J. A. Bessa, P. C. Cortez, J. H. da Silva F´elix, A. R. da Rocha Neto, and A. R. de Alexandria, “Radial snakes: comparison of segmentation methods in synthetic noisy images,†Expert Systems with Applications, vol. 42, no. 6, pp. 3079–3088, 2015.

      [36] N. C. F. Codella, J.W.Weinsaft,M. D. Cham, M. Janik, M. R. Prince, and Y.Wang, “Left ventricle: automated segmentation by using myocardial effusion threshold reduction and intra voxel computation at MR imaging,†Radiology, vol. 248, no. 3, pp. 1004–1012, 2008.

      [37] M.A.AlAttar,N. F.Osman, and A. S. Fahmy, “Segmentation of left ventricle in cardiac MRI images using adaptive multi-seeded region growing,†in Proceedings of the 5th Cairo International Biomedical Engineering Conference (CIBEC ’10), pp. 25–28, December 2010.

      [38] S. A. Tsaftaris, V. Andermatt, A. Schlegel, A. K. Katsaggelos, D. Li, and R. Dharma kumar, “A dynamic programming solution to tracking and elastically matching left ventricular walls in cardiac cine MRI,†in Proceedings of the IEEE International Conference on Image Processing (ICIP ’08), pp. 2980– 2983, San Diego, Calif, USA, October 2008.

      [39] A.Goshtasby and D. A.Turner, “Fusion of short-axis and long-axis cardiac MR images,†Computerized Medical Imaging and Graphics, vol.20, no. 2, pp.77–87, 1996.

      [40] D. Saring, J. Relan, M. Groth, K. M¨ullerleile, and H. Handels, “Combination of short- and longaxis MR image sequences for the 3D segmentation of the left ventricle,†Studies in Health Technology and Informatics, vol. 136, pp. 333–338, 2008.

      [41] D. S¨aring, J. Relan, M. Groth, K. M¨ullerleile, and H. Handels, “3D segmentation of the left ventricle combining long- and short-axis MR imagesâ€, Methods of Information in Medicine, vol. 48, no. 4, pp. 340–343, 2009.

      [42] D. Turco, C. Corsi, and C. Lamberti, “Fully automated quantification of left and right ventricular volumes throughout the cardiac cycle from magnetic resonance imaging,†in Proceedings of the Computing in Cardiology (CinC ’11), pp. 377–380, September 2011.

      [43] John E McManigle, Richard V Stebbing, J Alison Noble, “Modified Hough Transform For Left Ventricle Myocardium Segmentation In 3-D Echocardiogram Imagesâ€, BioMedIA Lab, Institute of Biomedical Engineering, Dept. of Eng. Science, University of Oxford.

      [44] P. Bansod, U. B Desai, and N. Burkule, “Automatic detection of left ventricle in echocardiographic sequences using radial search andtemporalsmoothing,â€in2007,15thInternationalConferenceonDigitalSignalProcessing, July 2007, pp. 139–142, IEEE.

      [45] Antonio Fernndez-Caballero and Jos M. Vega-Riesco, “Determining heart parameters through left ventricular automatic segmentation for heart disease diagnosis,†Expert Syst. Appl., vol. 36, no. 2, pp, 2234-2249, Mar.2009.

      [46] Shapiro, Linda and Stockman, George. "Computer Vision", Prentice-Hall, Inc. 2001.

      [47] T.JAtherton, D.J. Kerbyson. "Size invariant circle detection." Image and Vision Computing. Volume 17, Number 11, 1999, pp. 795-803.

      [48] Uday Kurkure, Amol Pednekar, Raja Muthupillai, Scott D. Flamm, and Ioannis A. Kakadiaris, “Localization and Segmentation of Left Ventricle in Cardiac Cine-MR Imagesâ€, IEEE Transactions On Biomedical Engineering, Vol. 56, No. 5, May 2009.

      [49] T. Padmapriya and V. Saminadan, “Priority based fair resource allocation and Admission Control Technique for Multi-user Multi-class downlink Traffic in LTE-Advanced Networksâ€, International Journal of Advanced Research, vol.5, no.1, pp.1633-1641, January 2017.

      [50] S.V.Manikanthan and D.Sugandhi “ Interference Alignment Techniques For Mimo Multicell Based On Relay Interference Broadcast Channel †International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume- 7 ,Issue 1 –MARCH 2014.

      [51] Rajesh, M., and J. M. Gnanasekar. "Congestion control in heterogeneous wireless ad hoc network using FRCC." Australian Journal of Basic and Applied Sciences 9.7 (2015): 698-702.

  • Downloads

  • How to Cite

    M., I., & S., K. (2017). Inevitable dissection of left ventricle aimed at discovery of cardiac blood flow appraisal. International Journal of Engineering & Technology, 7(1.5), 152-157. https://doi.org/10.14419/ijet.v7i1.5.9138

    Received date: 2018-01-11

    Accepted date: 2018-01-11

    Published date: 2017-12-31