Share This Article:

A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients

Abstract Full-Text HTML XML Download Download as PDF (Size:2599KB) PP. 152-167
DOI: 10.4236/jemaa.2015.75017    4,444 Downloads   5,146 Views   Citations

ABSTRACT

Microwave technology offers the possibility for pre-hospital stroke detection as we have previously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diagnostics. A priori information can be obtained by segmenting the patient’s head tissues from magnetic resonance images. Expert manual segmentation is presently the gold standard, but it is laborious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real data from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accuracy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accuracy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intracerebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intracerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmentation can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Mahmood, Q. , Li, S. , Fhager, A. , Candefjord, S. , Chodorowski, A. , Mehnert, A. and Persson, M. (2015) A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients. Journal of Electromagnetic Analysis and Applications, 7, 152-167. doi: 10.4236/jemaa.2015.75017.

References

[1] The Atlas of Heart Disease and Stroke.
http://www.who.int/cardiovascular_diseases/resources/atlas/en/
[2] Fassbender, K., Balucani, C., Walter, S., Levine, S.R., Haass, A. and Grotta, J. (2013) Streamlining of Prehospital Stroke Management: The Golden Hour. Lancet Neurology, 12, 585-596.
http://dx.doi.org/10.1016/S1474-4422(13)70100-5
[3] Burns, J.D., Green, D.M., Metivier, K. and DeFusco, C. (2012) Intensive Care Management of Acute Ischemic Stroke. Emergency Medicine Clinics of North America, 30, 713-744.
http://dx.doi.org/10.1016/j.emc.2012.05.002
[4] Feigin, V.L., Lawes, C.M., Bennett, D.A., Barker-Collo, S.L. and Parag, V. (2009) Worldwide Stroke Incidence and Early Case Fatality Reported in 56 Population-Based Studies: A Systematic Review. Lancet Neurology, 8, 355-369.
http://dx.doi.org/10.1016/S1474-4422(09)70025-0
[5] Chalela, J., Kidwell, C.S., Nentwich, L.M., Luby, M., Butman, J.A., et al. (2007) Magnetic Resonance Imaging and Computer Tomography in Emergency Assessment of Patients with Suspected Acute Stroke: A Prospective Comparison. Lancet, 369, 293-298.
http://dx.doi.org/10.1016/S0140-6736(07)60151-2
[6] Walter, S., Kostopoulos, P., Haass, A., Keller, I., Lesmeister, M., et al. (2012) Diagnosis and Treatment of Patients with Stroke in a Mobile Stroke Unit versus in Hospital: A Randomised Controlled Trial. Lancet Neurology, 11, 397-404.
http://dx.doi.org/10.1016/S1474-4422(12)70057-1
[7] Holscher, T., Dunford, J.V., Schlachetzki, F., Boy, S., Hemmen, T., et al. (2013) Prehospital Stroke Diagnosis and Treatment in Ambulances and Helicopters—A Concept Paper. American Journal of Emergency Medicine, 31, 743-747.
http://dx.doi.org/10.1016/j.ajem.2012.12.030
[8] Ireland, D. and Bialkowski, M. (2011) Microwave Head Imaging for Stroke Detection. Progress in Electromagnetics Research M, 21, 163-175.
http://dx.doi.org/10.2528/PIERM11082907
[9] Scapaticci, R., Di Donato, L., Catapano, I. and Crocco, L. (2012) A Feasibility Study on Microwave Imaging for Brain Stroke Monitoring. Progress in Electromagnetics Research B, 40, 305-324.
http://dx.doi.org/10.2528/PIERB12022006
[10] Irishina, N. and Torrente, A. (2013) Brain Stroke Detection by Microwaves Using Prior Information from Clinical Databases. Abstract and Applied Analysis, 2013, Article ID: 412638.
http://dx.doi.org/10.1155/2013/412638
[11] Persson, M., Fhager, A., Trefna, H.D., Yu, Y.N., McKelvey, T., Pegenius, G., et al. (2014) Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible. IEEE Transactions on Biomedical Engineering, 61, 2806-2817.
http://dx.doi.org/10.1109/TBME.2014.2330554
[12] Dielectric Properties of Body Tissues.
http://niremf.ifac.cnr.it/tissprop/
[13] Smith, S.M. (2002) Fast Robust Automated Brain Extraction. Human Brain Mapping, 17, 143-155.
http://dx.doi.org/10.1002/hbm.10062
[14] Zhang, Y., Brady, M. and Smith, S. (2001) Segmentation of Brain MR Images through a Hidden Markov Random Field Model and the Expectation Maximization Algorithm. IEEE Transactions on Medical Imaging, 20, 45-57.
http://dx.doi.org/10.1109/42.906424
[15] Fhager, A. and Persson, M. (2007) Using a Priori Data to Improve the Reconstruction of Small Objects in Microwave Tomography. IEEE Transactions on Microwave Theory and Techniques, 55, 2454-2462.
http://dx.doi.org/10.1109/TMTT.2007.908670
[16] Cocosco, C.A., Kollokian, V., Kwan, R.K.S. and Evans, A.C. (1997) BrainWeb: Online Interface to 3-D MRI Simulated Brain Database. NeuroImage, 5, S425.
[17] IXI Dataset.
http://www.brain-development.org/
[18] Otsu, N. (1979) A Threshold Selection Method from Gray-Level Histrogram. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66.
http://dx.doi.org/10.1109/TSMC.1979.4310076
[19] Soille, P. (1999) Morphological Image Analysis: Principles and Applications. Springer-Verlag, Berlin, 173-174.
http://dx.doi.org/10.1007/978-3-662-03939-7
[20] Mayer, A. and Greenspan, H. (2009) An Adaptive Mean-Shift Framework for MRI Brain Segmentation. IEEE Transactions on Medical Imaging, 28, 1238-1249.
http://dx.doi.org/10.1109/TMI.2009.2013850
[21] Mahmood, Q., Chodorowski, A., Mehnert, A. and Persson, M. (2012) A Novel Bayesian Approach to Adaptive Mean Shift Segmentation of Brain Images. Proceedings of the IEEE International Symposium on Computer-Based Medical Systems (CBMS), Rome, 20-22 June 2012, 1-6.
http://dx.doi.org/10.1109/CBMS.2012.6266304
[22] Seber, G.A.F. (1984) Multivariate Observations. John Wiley & Sons, Inc., Hoboken.
http://dx.doi.org/10.1002/9780470316641
[23] Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell.
http://dx.doi.org/10.1007/978-1-4757-0450-1
[24] Comaniciu, D., Ramesh, V. and Meer, P. (2001) The Variable Bandwidth Mean-Shift and Data-Driven Scale Selection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Vancouver, 7-14 July 2001, 438-445.
http://dx.doi.org/10.1109/ICCV.2001.937550
[25] Comaniciu, D. and Meer, P. (2002) Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619.
http://dx.doi.org/10.1109/34.1000236
[26] Shirvany, Y., Edelvik, F., Jakobsson, S., Hedstrom, A., Mahmood, Q., Chodorowski, A. and Persson, M. (2012) Non-Invasive EEG Source Localization Using Particle Swarm Optimization: A Clinical Experiment. Proceedings of the 34th Annual International Conference of the IEEE EMBS, San Diego, 28 August-1 September 2012, 6232-6235.
[27] Rullmann, M., Anwander, A., Dannhauer, M., Warfield, S.K., Duffy, F. and Wolters, C. (2009) EEG Source Analysis of Epileptiform Activity Using a 1 mm Anisotropic Hexahedra Finite Element Head Model. NeuroImage, 44, 399-410.
http://dx.doi.org/10.1016/j.neuroimage.2008.09.009
[28] Lanfer, B., Scherg, M., Dannhauer, M., Knosche, T.R., Burger, M., Wolters, C.H. (2012) Influences of Skull Segmentation Inaccuracies on EEG Source Analysis. NeuroImage, 62, 418-431.
http://dx.doi.org/10.1016/j.neuroimage.2012.05.006
[29] Fhager, A. (2006) A Microwave Tomography. Doktorsavhandlingar vid Chalmers tekniska hogskola, Gothenburg.
[30] Fhager, A., Hashemzadeh, P. and Persson, M. (2006) Reconstruction Quality and Spectral Content of an Electromagnetic Time-Domain Inversion Algorithm. IEEE Transactions on Biomedical Engineering, 53, 1594-1604.
http://dx.doi.org/10.1109/TBME.2006.878079
[31] Semenov, S.Y., Bulyshev, A.E., Abubakar, A., Posukh, V.G., Sizov, Y.E., Souvorov, A.E., et al. (2005) Microwave-Tomographic Imaging of the High Dielectric-Contrast Objects Using Different Image-Reconstruction Approaches. IEEE Transactions on Microwave Theory and Techniques, 53, 2284-2294.
http://dx.doi.org/10.1109/TMTT.2005.850459
[32] Slaney, M., Kak, A.C. and Larsen, L.E. (1984) Limitations of Imaging with First-Order Diffraction Tomography. IEEE Transactions on Microwave Theory and Techniques, 32, 860-874.
http://dx.doi.org/10.1109/TMTT.1984.1132783
[33] Pahomov, V., Semenchik, V. and Kurilo, S. (2005) Reconstructing Reflecting Object Images Using Born Approximation. Proceedings of the IEEE 35th European Microwave Conference, Paris, 4-6 October 2005.
[34] Larsen, L.E. and Jacobi, J.H. (1986) Medical Applications of Microwave Imaging. IEEE Press, Piscataway.
[35] Tikhonov, A.N. and Arsenin, V.Y. (1977) Solutions of Ill-Posed Problems. Winston and Sons, Washington DC.
[36] Dice, L.R. (1945) Measures of the Amount of Ecologic Association between Species. Ecology, 26, 297-302.
http://dx.doi.org/10.2307/1932409

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.