Skip to main content

2018 | OriginalPaper | Buchkapitel

Local Region with Optimized Boundary Driven Level Set Based Segmentation of Myocardial Ischemic Cardiac MR Images

verfasst von : M. Muthulakshmi, G. Kavitha

Erschienen in: Advanced Computational and Communication Paradigms

Verlag: Springer Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this work, an attempt is made to segment endocardium and epicardium of left ventricle in normal and myocardial ischemic cardiac magnetic resonance (CMR) images using local region with optimized boundary driven level set. Myocardial ischemia (MI) is a cardiac disorder that results in deprivation of oxygen supply to myocardium and can be analyzed by study of abnormal anatomical changes in CMR. This study is carried out on short-axis view CMR images from Medical Image Computing and Computer-Assisted Intervention (MICCAI) database. The edges are computed by simple Laplacian and Laplacian of Gaussian (LOG) operator. LOG is optimized to obtain enhanced edges of endocardium and epicardium. The quality of edge is validated with edge preservation index (EPI) and gradient magnitude similarity deviation (GMSD) measure. Local region with optimized boundary (LROB) driven level set is utilized for simultaneous segmentation of endocardium and epicardium of left ventricle in CMR images. The results are compared with local region (LR) driven and LR with LOG-driven level set. Further, the efficacy of the segmentation is validated with different similarity measures. The optimized LOG image visually shows better endocardium and epicardium contours. Optimized LOG with a higher EPI and lower GMSD provides better enhanced edges compared to Laplacian and LOG functions. The computed similarity measures for LR with LOG-driven level set are significantly higher compared to LR-based level set for segmentation of endocardium and epicardium. Further, LROB-driven level set shows higher similarity measures than LR with LOG-driven level set. Thus, LROB-driven level set provides better segmentation accuracy for epicardium and endocardium of left ventricle than LR-based level set and LR with LOG-driven level set. The efficiently segmented endocardium and epicardium could aid the diagnosis of myocardial ischemia with their ability to quantify anatomical changes in LV.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
2.
Zurück zum Zitat Suinesiaputra, A., McCulloch, A.D., Nash, M.P., Pontre, B., Young, A.A.: Cardiac image modelling: breadth and depth in heart disease. Med. Image Anal. 33, 38–43 (2016)CrossRef Suinesiaputra, A., McCulloch, A.D., Nash, M.P., Pontre, B., Young, A.A.: Cardiac image modelling: breadth and depth in heart disease. Med. Image Anal. 33, 38–43 (2016)CrossRef
3.
Zurück zum Zitat Dakua, S.P., Sahambi, J.S.: Detection of left ventricular myocardial contours from ischemic cardiac MR images. IETE J. Res. 57(4), 372–384 (2014)CrossRef Dakua, S.P., Sahambi, J.S.: Detection of left ventricular myocardial contours from ischemic cardiac MR images. IETE J. Res. 57(4), 372–384 (2014)CrossRef
4.
Zurück zum Zitat Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2011)CrossRef Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2011)CrossRef
5.
Zurück zum Zitat Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mater. Phys., Biol. Med. 29(2), 155–195 (2016)CrossRef Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mater. Phys., Biol. Med. 29(2), 155–195 (2016)CrossRef
6.
Zurück zum Zitat Hu, H., Liu, H., Gao, Z., Huang, L.: Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming. Magn. Reson. Imaging 31(4), 575–584 (2013)CrossRef Hu, H., Liu, H., Gao, Z., Huang, L.: Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming. Magn. Reson. Imaging 31(4), 575–584 (2013)CrossRef
7.
Zurück zum Zitat Huang, S., Liu, J., Lee, L.C., Venkatesh, S.K., San Teo, L.L., Au, C., Nowinski, W.L.: An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images. J. Digit. Imaging 24(4), 598–608 (2011)CrossRef Huang, S., Liu, J., Lee, L.C., Venkatesh, S.K., San Teo, L.L., Au, C., Nowinski, W.L.: An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images. J. Digit. Imaging 24(4), 598–608 (2011)CrossRef
8.
Zurück zum Zitat Kaus, M.R., von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 8(3), 245–254 (2004)CrossRef Kaus, M.R., von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 8(3), 245–254 (2004)CrossRef
9.
Zurück zum Zitat Folkesson, J., Samset, E., Kwong, R.Y., Westin, C.F.: Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI. IEEE Trans. Inf Technol. Biomed. 12(3), 328–334 (2008)CrossRef Folkesson, J., Samset, E., Kwong, R.Y., Westin, C.F.: Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI. IEEE Trans. Inf Technol. Biomed. 12(3), 328–334 (2008)CrossRef
10.
Zurück zum Zitat Leiner, B.J., Olveres, J., Ramírez, B.E., Arámbula, F., Vallejo, E.: Segmentation of 4D cardiac computed tomography images using active shape models. In: SPIE 8436 Optics Photonics and Digital Technologies for Multimedia Applications II 84361E (2012) Leiner, B.J., Olveres, J., Ramírez, B.E., Arámbula, F., Vallejo, E.: Segmentation of 4D cardiac computed tomography images using active shape models. In: SPIE 8436 Optics Photonics and Digital Technologies for Multimedia Applications II 84361E (2012)
11.
Zurück zum Zitat Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19(1), 98–109 (2015)CrossRef Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19(1), 98–109 (2015)CrossRef
12.
Zurück zum Zitat Avendi, M.R., Kheradvar, A., Jafarkhani, H.: Combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)CrossRef Avendi, M.R., Kheradvar, A., Jafarkhani, H.: Combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)CrossRef
13.
Zurück zum Zitat Ammar, M., Mahmoudi, S., Chikh, M.A., Abbou, A.: Endocardial border detection in cardiac magnetic resonance images using level set method. J. Digit. Imaging 25, 294–306 (2012)CrossRef Ammar, M., Mahmoudi, S., Chikh, M.A., Abbou, A.: Endocardial border detection in cardiac magnetic resonance images using level set method. J. Digit. Imaging 25, 294–306 (2012)CrossRef
14.
Zurück zum Zitat Li, C., Huang, R., Ding, Z., Gatenby, C., Metaxas, D., Gore, J.: A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. Med. Image Comput. Comput. Assist. Interv. 11(Pt 2), 1083–1091 (2008) Li, C., Huang, R., Ding, Z., Gatenby, C., Metaxas, D., Gore, J.: A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. Med. Image Comput. Comput. Assist. Interv. 11(Pt 2), 1083–1091 (2008)
15.
Zurück zum Zitat Liu, Y., Captur, G., Moon, J.C., Guo, S., Yang, X., Zhang, S., Li, C.: Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI. Magn. Reson. Imaging 34(5), 699–706 (2016)CrossRef Liu, Y., Captur, G., Moon, J.C., Guo, S., Yang, X., Zhang, S., Li, C.: Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI. Magn. Reson. Imaging 34(5), 699–706 (2016)CrossRef
16.
Zurück zum Zitat Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med. Image Anal. 35, 159–171 (2017)CrossRef Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med. Image Anal. 35, 159–171 (2017)CrossRef
17.
Zurück zum Zitat Ding, K., Xiao, L., Weng, G.: Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Sig. Process. 134, 224–233 (2017)CrossRef Ding, K., Xiao, L., Weng, G.: Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Sig. Process. 134, 224–233 (2017)CrossRef
18.
Zurück zum Zitat Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A.J., Wright, G.A.: Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. Card. MR Left Ventricle Segmentation Challenge. http://hdl.handle.net/10380/3070 Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A.J., Wright, G.A.: Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. Card. MR Left Ventricle Segmentation Challenge. http://​hdl.​handle.​net/​10380/​3070
19.
Zurück zum Zitat Kuppusamy, P.G., Joseph, J., Sivaraman, J.A.: A full reference Morphological Edge Similarity Index to account processing induced edge artefacts in magnetic resonance images. Biocybern. Biomed. Eng. 37, 159–166 (2017)CrossRef Kuppusamy, P.G., Joseph, J., Sivaraman, J.A.: A full reference Morphological Edge Similarity Index to account processing induced edge artefacts in magnetic resonance images. Biocybern. Biomed. Eng. 37, 159–166 (2017)CrossRef
20.
Zurück zum Zitat Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)MathSciNetCrossRef Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)MathSciNetCrossRef
21.
Zurück zum Zitat Raja, N.S.M., Kavitha, G., Ramakrishnan, S.: Analysis of vasculature in human retinal images using particle swarm optimization based tsallis multi-level thresholding and similarity measures. In: International Conference on Swarm Evolutionary and Memetic Computing—SEMCCO: Swarm Evolutionary and Memetic Computing, pp. 380–387 (2012)CrossRef Raja, N.S.M., Kavitha, G., Ramakrishnan, S.: Analysis of vasculature in human retinal images using particle swarm optimization based tsallis multi-level thresholding and similarity measures. In: International Conference on Swarm Evolutionary and Memetic Computing—SEMCCO: Swarm Evolutionary and Memetic Computing, pp. 380–387 (2012)CrossRef
Metadaten
Titel
Local Region with Optimized Boundary Driven Level Set Based Segmentation of Myocardial Ischemic Cardiac MR Images
verfasst von
M. Muthulakshmi
G. Kavitha
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-8237-5_2

Neuer Inhalt