Segmentation of an Ultrasonic Reflector Image Based on Analyzing the Proximity of Its Histogram to the Rayleigh Distribution
Source abstract
Abstract
Abstract Currently, reflector recognition (classification) systems based on artificial neural networks are being actively developed in order to increase the speed of preparation of the ultrasound testing protocol and reduce the influence of the human factor. For more efficient operation of neural networks, it is advisable to process images of reflectors in order to increase the signal-to-noise ratio of the image and its segmentation (clustering). The article proposes a segmentation method based on the construction of a binary mask that hides the reflector flare. The mask is created according to the condition of obtaining the image histogram with an appearance closest to the Rayleigh distribution. A genetic algorithm is used to solve the problem of finding the minimum. The model experiments show the effectiveness of this approach to segmentation of reflector images reconstructed from echo signals measured using antenna arrays. A method based on analyzing the flare amplitudes of images reconstructed using various acoustic schemes is used to determine the reflector type.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Computational Techniques in Science and Engineering · Image and Signal Denoising Methods · Advanced Scientific Research Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (19)
Show source-provided references
1. Advances in Phased Array Ultrasonic Technology Applications, Waltham: Olympus NDT, 2007. https://www.olympus-ims.com/en/resources/books. Accessed July 15, 2022.
2. Bazulin, E.G., Comparison of systems for ultrasonic nondestructive testing using antenna arrays or phased antenna arrays, Russ. J. Nondestr. Test., 2013, vol. 49, no. 7, pp. 404–423.10.1134/S1061830913070024 ↗
3. Bazulin, E.G., Vopilkin, A.Kh., and Tikhonov, D.S., Determining the type of ref lector from amplitudes of flares reconstructed in various acoustic schemes, Russ. J. Nondestr. Test., 2021, vol. 57, no. 4, pp. 251–260.10.1134/S1061830921040021 ↗
4. Badalyan, V.G. and Vopilkin, A.Kh., Application of neural networks in ultrasonic non-destructive testing (review), Kontrol’. Diagn., 2022, no. 5, pp. 12–25. https://doi.org/10.14489/td.2022.05.pp.012-02510.14489/td.2022.05.pp.012-025 ↗
5. Wikipedia: The Free Encyclopedia. https://en.wikipedia.org/wiki/Image_segmentation. Accessed July 12, 2022.
6. Perona, P. and Malik, J., Scale space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 1990, vol. 12, no. 6, pp. 629–639.10.1109/34.56205 ↗
7. Ben Mhamed, I., Abid, S., and Fnaiech, F., Weld defect detection using a modified anisotropic diffusion model, EURASIP J. Adv. Signal Process., 2012, p. 46. https://doi.org/10.1186/1687-6180-2012-4610.1186/1687-6180-2012-46 ↗
8. Artyukov, I.A. and Irtuganov, N.N., Noise-driven anisotropic diffusion filtering for X-ray low contrast imaging, J. Russ. Laser Res., 2019, vol. 40, no. 3, pp. 150–154. https://doi.org/10.1007/s10946-019-09782-810.1007/s10946-019-09782-8 ↗
9. Bazulin, E.G., Application of adaptive anisotropic diffusion filter to improve the reflector image quality when performing ultrasonic nondestructive testing, Russ. J. Nondestr. Test., 2021, vol. 57, no. 5, pp. 343–351.10.1134/S1061830921050041 ↗
10. Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Amsterdam: Kluwer Academic, 1981.10.1007/978-1-4757-0450-1 ↗
11. Haralick, R.M., Shanmugam, K., and Dinstein, I., Textural features for image classification, IEEE Trans. Syst. Man Cybern., 1973, vol. SMC-3, no. 6, pp. 610–621. https://doi.org/10.1109/TSMC.1973.430931410.1109/TSMC.1973.4309314 ↗
12. Tizhoosh, H.R., Type II fuzzy image segmentation, in: Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, Bustince, H., Herrera, F., Montero, J., Eds., Berlin: Springer, 2008, vol. 220. https://doi.org/10.1007/978-3-540-73723-0_3110.1007/978-3-540-73723-0_31 ↗
13. Otsu, N., A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 1979, vol. 9, no. 1, pp. 62–66.10.1109/TSMC.1979.4310076 ↗
14. Neurohive. https://neurohive.io/ru/papers/deep-claster/. Accessed July 12, 2022.
15. Wikipedia: Rayleigh distribution. https://en.wikipedia.org/wiki/Rayleigh_distribution. Accessed July 12, 2022.
16. Bevan, R., Zhang, J., Budyn, N., Croxford, A.J., and Wilcox, P.D., Experimental quantification of noise in linear ultrasonic imaging, Trans. Ultrason. Ferroelectr. Freq. Cont., 2018, vol. 66, no. 1, pp. 79–90. https://doi.org/10.1109/TUFFC.2018.287472010.1109/TUFFC.2018.2874720 ↗
17. Johnson, N.L., Kotz, S., and Kemp, A.U., Univariate Discrete Distributions, New York: Wiley, 1993.
18. Fraser, A.S., Simulation of genetic systems, J. Theor. Biol., 1962, vol. 2, pp. 329–346.10.1016/0022-5193(62)90036-X ↗
19. Research and Production Center “ECHO +”: the official website of the company. http://www.echoplus.ru. Accessed July 15, 2022.