Microwave Medical Image Segmentation For Brain Stroke Diagnosis: Imaging-Process-Informed Image Processing
Keywords:
Microwave Imaging, Brain Stroke Diagnosis, Image Segmentation, Distorted Born Iterative Method (DBIM), Dielectric Constant ReconstructionAbstract
We propose a novel imaging-process informed image segmentation method that accounts for uncertainty during the imaging process. A priori information is incorporated to enhance the contrast between stroke area and healthy tissues. The distorted Born iterative method (DBIM) is utilized to reconstruct the stroke area of the brain. Due to the nonlinear relationship between actual and estimated dielectric constants resulting from DBIM, the microwave medical image lacks a clearly defined boundary, posing a challenge to accurately segment it using traditional methods. The proposed method achieves effective image segmentation by improving the traditional threshold method. From the simulation results, the region miss classified by the traditional method accounts for 89%, while the proposed method results in a miss classification rate of only 13%. The results demonstrate a significant improvement of 58.85% in accurately reproducing the dielectric constants.
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V. L. Feigin, B. Norrving, G. A. Mensah, “Global burden of stroke,” Circul. Res., vol. 120, no. 3, pp. 439-448, 2017.
M. Giroud, A. Jacquin, Y. Béjot, “The worldwide landscape of stroke in the 21st century,” Lancet, vol. 383, no. 9913, pp. 195, 2014.
D. J. Mollura, M. P. Lungren, “Radiology in global health: strategies, implementation, and applications,” Springer, 2014.
L. Baker, S. Atlas, C. Afendulis, “Expanded use of imaging technology and the challenge of measuring value,” Health Aff, vol. 27, no. 6, pp.1467- 1478, 2008.
J. L. Saver, “Time is brain: quantified,” Stroke, vol. 37, no. 1, pp. 263- 266, 2006.
Z. Gong, Y. Chen, X. Lin, M. J. Cree, “Contrast-enhanced microwave cancer detection using angle-of-arrival approach,” IEEE Trans. Antennas Propag., vol. 70, no. 5, pp. 3772-3780, 2021.
C. Qin, Z. Zhao, Y. Li, “Recent advances in microwave imaging for breast cancer detection,” IEEE Access, vol. 6, pp. 55885-55906, 2018.
B. J. Mohammed, A. M. Abbosh, S. Mustafa, “Microwave system for he ad imaging,” IEEE Trans. Instrum. Meas., vol. 63, no. 1, pp. 117-123, 20 13.
F. Gao, B. D. Van Veen, S. C. Hagness, “Sensitivity of the distorted born iterative method to the initial guess in microwave breast imaging,” IEEE Trans. Antennas Propag., vol. 63, no. 8, pp. 3540-3547, 2015.
D. L. Pham, C. Xu, J. L. Prince, “Annual review of biomedical engineering”, Springer, 2000.
J. A. Martinez-Lorenzo, J. L. Gomez-Tornero, C. R. Salinas, “Microwave imaging for medical applications: Current techniques, challenges, and future trends,” IEEE Trans. Microw. Theory Techn., vol. 65, no. 7, pp. 2387-2403, 2017
C. Qin, Y. Li, “Review of microwave imaging based on the Born and Ry tov approximations: When to use which?,” J. Electromagn. Waves App., vol. 31, no. 5, pp. 463-481, 2017.
P. M. Van den Berg, H. R. Kroeze, P. W. Verbeek, “Comparison of linear and non-linear image reconstruction algorithms for microwave breast imaging,” Med. Biol. Engin. Com., vol. 53, no. 5, pp. 391-402, 2015.
B. De Smedt, L. Crocco, D. J. Hawkes, “Comparison of different inversio n strategies for microwave breast imaging,” IEEE Trans. Med. Imag., vol. 36, no. 9, pp. 1835-1846, 2017.
X. Zhang, P. M. Meaney, “A method for tissue segmentation and bounda ry detection in microwave imaging,” IEEE Trans. Biomed. Engin., vol. 5 6, no. 10, pp. 2429-2436, 2009.
I. G. Zubal, C. R. Harrell, E. O. Smith, Z. Rattner, G. Gindi, and P. B. Hoffer, “Computerized three-dimensional segmented human anatomy,” Med. Phys., vol. 21, no. 2, pp. 299-302, 1994.
Z. Gong, Y. Chen, X. Lin, K. Wu, M. J. Cree, “Generic wideband phantom design methodology for microwave medical applications,” IEEE Antennas Wireless Propag. Lett., vol. 20, no. 8, pp. 1488-1492, 2021.
B. Ramesh Chandra, G Raghupal Reddy, A. Shravan Kumar “ANALYSIS AND PREDICTION OF COVID-19 USING REGRESSION MODELS AND TIME SERIESFORECASTING,” ijaema vol. 13, no. 1 pp. 2289-2296, 2021.
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