Hide
Show all
Email this article Echocardiogram Image Quality Enhancement using Upsampling and Histogram Matching Methods
(1) Institut Teknologi Bandung
(2) Institut Teknologi Bandung
Abstract
The prevalence of heart disease has been increasing in the last ten years. One of the cardiac diagnostic tools is echocardiography. Echocardiogram medical images provide essential information, including shape, size, pumping capacity, heart function abnormalities, and location of heart damage, but echocardiogram images have high noise content and poor contrast, as well as limitations due to differences in anatomy or body mass. This will affect the reading results of patient diagnosis. Therefore, image quality improvement is needed by removing noise and increasing image contrast. This research has improved image quality using a method with low mathematical complexity and a fast computational process. The method used is the Upsampling method to generate a reference image. The quality of the image produced was the Nearest Neighbor upsampling method: 2.8 dB, Bi-linear Interpolation: 2.78 dB, and Bi-cubic Interpolation: 2.73 dB. Furthermore, the image with the highest SNR value is processed with Histogram Matching to accelerate improving image quality. The Histogram Matching image increases quality by more than 50% with a SSIM value of 0.54. The required computational process to apply this method to each medical image has an average duration of 0.4 s. This result provides a higher value than several methods using linear scaling and speckle reducing.
Keywords
Full Text:
PDFReferences
F. Laumer, M. Amrani, L. Manduchi, A. Beuret, L. Rubi, A. Dubatovka, C. M. Matter, and J. M. Buhmann, “Weakly supervised inference of personalized heart meshes based on echocardiography videos,” Medical Image Analysis, vol. 83, p. 102653, Jan. 2023, doi: https://doi.org/10.1016/j.media.2022.102653.
W. X. Chan, Y. Zheng, H. Wiputra, H. L. Leo, and C. H. Yap, “Full cardiac cycle asynchronous temporal compounding of 3D echocardiography images,” Medical Image Analysis, vol. 74, p. 102229, Dec. 2021, doi: https://doi.org/10.1016/j.media.2021.102229.
M. C. Pastore, G. E. Mandoli, H. S. Aboumarie, C. Santoro, F. Bandera, A. D’Andrea, G. Benfari, R. Esposito, V. Evola, R. Sorrentino, P. Cameli, S. Valente, S. Mondillo, M. Galderisi, and M. Cameli, “Basic and advanced echocardiography in advanced heart failure: an overview,” Heart Failure Reviews, vol. 25, no. 6, pp. 937–948, Oct. 2019, doi: https://doi.org/10.1007/s10741-019-09865-3.
M. H. Picard and R. B. Weiner, “Echocardiography in the Time of COVID-19,” Journal of the American Society of Echocardiography, Apr. 2020, doi: https://doi.org/10.1016/j.echo.2020.04.011.
D. Muraru, A. Niero, H. Rodriguez-Zanella, D. Cherata, and L. Badano, “Three-dimensional speckle-tracking echocardiography: benefits and limitations of integrating myocardial mechanics with three-dimensional imaging,” Cardiovascular Diagnosis and Therapy, vol. 8, no. 1, pp. 101–117, Feb. 2018, doi: https://doi.org/10.21037/cdt.2017.06.01.
H. Bu, Y. Yang, Q. Wu, W. Jin, and T. Zhao, “Echocardiography-guided percutaneous closure of perimembranous ventricular septal defects without arterial access and fluoroscopy,” BMC Pediatrics, vol. 19, no. 1, Aug. 2019, doi: https://doi.org/10.1186/s12887-019-1687-0.
Z. Akkus, Y. H. Aly, I. Z. Attia, F. Lopez-Jimenez, A. M. Arruda-Olson, P. A. Pellikka, S. V. Pislaru, G. C. Kane, P. A. Friedman, and J. K. Oh, “Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review,” Journal of Clinical Medicine, vol. 10, no. 7, p. 1391, Mar. 2021, doi: https://doi.org/10.3390/jcm10071391.
D. Dumitrescu and Costin-Anton Boiangiu, “A Study of Image Upsampling and Downsampling Filters,” Computers, vol. 8, no. 2, pp. 30–30, Apr. 2019, doi: https://doi.org/10.3390/computers8020030.
M. A. Molina-Cabello, J. García-González, Rafael Marcos Luque-Baena, and E. López-Rubio, “The effect of downsampling–upsampling strategy on foreground detection algorithms,” Artificial Intelligence Review, vol. 53, no. 7, pp. 4935–4965, Feb. 2020, doi: https://doi.org/10.1007/s10462-020-09811-y.
R. Roy, S. Ghosh, and A. Ghosh, “Clinical ultrasound image standardization using histogram specification,” Comput Biol Med, vol. 120, no. November 2019, p. 103746, 2020, doi: 10.1016/j.compbiomed.2020.103746.
J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, and D. Rueckert, “Attention gated networks: Learning to leverage salient regions in medical images,” Medical Image Analysis, vol. 53, pp. 197–207, Apr. 2019, doi: 10.1016/j.media.2019.01.012.
L. Jain and P. Singh, “A novel wavelet thresholding rule for speckle reduction from ultrasound images,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4461–4471, 2022, doi: 10.1016/j.jksuci.2020.10.009.
R. Roy, S. Ghosh, and A. Ghosh, “Clinical ultrasound image standardization using histogram specification,” Computers in Biology and Medicine, vol. 120, pp. 103746–103746, May 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103746.
H. Choi and J. Jeong, “Speckle noise reduction in ultrasound images using SRAD and guided filter,” 2018 International Workshop on Advanced Image Technology, IWAIT 2018, pp. 1–4, 2018, doi: 10.1109/IWAIT.2018.8369653.
P. C. Tay, C. D. Garson, S. T. Acton, and J. A. Hossack, “Ultrasound despeckling for contrast enhancement,” IEEE Transactions on Image Processing, vol. 19, no. 7, pp. 1847–1860, 2010, doi: 10.1109/TIP.2010.2044962.
M. Nadeem, A. Hussain, and A. Munir, “Fuzzy logic based computational model for speckle noise removal in ultrasound images,” Multimed Tools Appl, vol. 78, no. 13, pp. 18531–18548, 2019, doi: 10.1007/s11042-019-7221-4.
F. Baselice, G. Ferraioli, V. Pascazio, and A. Sorriso, “Denoising of MR images using Kolmogorov-Smirnov distance in a Non Local framework,” Magn Reson Imaging, vol. 57, no. July 2018, pp. 176–193, 2019, doi: 10.1016/j.mri.2018.11.022.
A. Velichko, E. L. Villaverde, and A. J. Croxford, “Local scattering ultrasound imaging,” Sci Rep, vol. 11, no. 1, 2021, doi: 10.1038/s41598-020-79617-z.
F. Baselice, “Ultrasound Image Despeckling Based on Statistical Similarity,” Ultrasound Med Biol, vol. 43, no. 9, pp. 2065–2078, 2017, doi: 10.1016/j.ultrasmedbio.2017.05.006.
Z. Liu, X. Yang, R. Gao, S. Liu, H. Dou, S. He, Y. Huang, H. Luo, Y. Zhang, Y. Xiong, and D. Ni, “Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer,” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Apr. 2020, doi: https://doi.org/10.1109/isbi45749.2020.9098457.
N. Bottenus, B. C. Byram, and D. Hyun, “Histogram Matching for Visual Ultrasound Image Comparison,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 5, pp. 1487–1495, May 2021, doi: https://doi.org/10.1109/tuffc.2020.3035965.
P. Thévenaz, T. Blu, and M. Unser, “Interpolation revisited,” IEEE Trans Med Imaging, vol. 19, no. 7, pp. 739–758, 2000, doi: 10.1109/42.875199.
A. Y. Bequet, L. Rusyadi, and F. Fatimah, “Nilai Contrast to Noise Ratio (CNR) Radiograf Thorax PA antara menggunakan Grid dengan tanpa Menggunakan Grid,” Jurnal Imejing Diagnostik (JImeD), vol. 6, no. 2, pp. 60–64, Jul. 2020, doi: https://doi.org/10.31983/jimed.v6i2.5653.
A. Degerli, M. Zabihi, S. Kiranyaz, T. Hamid, R. Mazhar, R.Hamila, and M. Gabbouj, “Early Detection of Myocardial Infarction in Low-Quality Echocardiography,” IEEE Access, vol. 9, pp. 34442–34453, 2021, doi: https://doi.org/10.1109/access.2021.3059595
A. Degerli, S. Kiranyaz, T. Hamid, R. Mazhar, and M. Gabbouj, “Early Myocardial Infarction Detection over Multi-view Echocardiography,” arXiv.org, Feb. 26, 2023. https://arxiv.org/abs/2111.05790v3 (accessed Jun. 15, 2023).
S. Kiranyaz, A. Degerli, T. Hamid, R. Mazhar, R. E. Fadil Ahmed, R. Abouhasera, M. Zabihi, J. Malik, R. Hamila, and M. Gabbouj, “Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection,” IEEE Access, vol. 8, pp. 210301–210317, 2020, doi: https://doi.org/10.1109/access.2020.3038743.
L. T. Nguyen, D. H. Vu, N. C. Vu, V. H. Dao, and T. H. Tran, “Comparative study on super resolution techniques for upper gastrointestinal endoscopic images,” ICCE 2022 - 2022 IEEE 9th International Conference on Communications and Electronics, pp. 375–380, 2022, doi: 10.1109/ICCE55644.2022.9852031.
S. Ameer, “Histogram Matching Schemes for Image Thresholding,” American Journal of Engineering and Applied Sciences, vol. 12, no. 3, pp. 413–419, 2019, doi: 10.3844/ajeassp.2019.413.419.
Y. Zhou, C. Shi, B. Lai, and G. Jimenez, “Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm,” Quant Imaging Med Surg, vol. 9, no. 9, pp. 1528–1547, 2019, doi: 10.21037/qims.2019.08.19.
Y. Shao, J. Wu, H. Ou, M. Pei, L. Liu, A. A. Movassagh, A. Sharma, G. Dhiman, M. Gheisari, and A. Asheralieva, “Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement,” Mathematical Problems in Engineering, vol. 2021, pp. 1–13, Mar. 2021, doi: https://doi.org/10.1155/2021/5580630
B. S. Rao, “Dynamic Histogram Equalization for contrast enhancement for digital images,” Applied Soft Computing Journal, vol. 89, p. 106114, 2020, doi: 10.1016/j.asoc.2020.106114.
Refbacks
- There are currently no refbacks.
