Abstract
Mixed noise suppression from color Doppler ultrasound (CDUS) images is always a challenging task because the noise distribution usually does not have a parametric model and heavy tail. It affects the inherent features of the image awkwardly. Consequently, identifying an internal blockage or hemorrhage of the patient becomes arduous in such conditions. An acquired CDUS image is majorly affected by speckle noise and can be coupled with Gaussian and impulse noises. In this paper, the evolutionary multichannel Jaya based functional link artificial neural network (named as M-Jaya-FLANN) has been proposed to get rid of mixed noise from the CDUS images. The subjective evaluation and the measurement of qualitative metrics, such as structural similarity index, computational time, convergence rate, and Friedman’s test are carried out for the performance analysis of different filters. The research outcomes exhibit the supremacy of the proposed filter over other competitive filters and can handle real-time noise elimination after completion of training. For the experimentation purpose, CDUS image data are collected from Medanta hospital, Ranchi, India.
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Loizou, C.P., Pattichis, C.S., Pantziaris, M., Tyllis, T., Nicolaides, A.: Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med. Biol. Eng. Comput. 44, 414–426 (2006)
Sudha, S., Suresh, G.R., Sukanesh, R.: Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding. IETE J. Res. 55(3), 135–143 (2009)
Rekha, C.K., K, M., Rao, G.V.S.: Speckle noise reduction in 3D ultrasound images—a review. In: IEEE Signal Processing and Communication Engineering Systems (SPACES), pp. 257–259 (2015)
Jai Jaganath Babu, J., Florence Sudha, G.: Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation. Biomed. Signal Process. Control 23, 93–103 (2016)
Zhao, H., Zeng, X., He, Z., Yu, S., Chen, B.: Improved functional link artificial neural network via convex combination for nonlinear active noise control. Appl. Soft Comput. J. 42, 351–359 (2016)
Das, S.R., Mishra, D., Rout, M.: A hybridized ELM-Jaya forecasting model for currency exchange prediction. J. King Saud Univ. – Comput. Inf. Sci. 32(3), 345–366 (2020)
Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans. Image Process. 23(6), 2651–2662 (2014)
Xiong, S., Zhou, Z., Member, S.: Neural filtering of colored noise based on Kalman filter structure. IEEE Trans. Instrum. Meas. 52(3), 742–747 (2003)
Barletta, L., Magarini, M., Spalvieri, A.: Bridging the gap between Kalman filter and Wiener filter in carrier phase tracking. IEEE Photonics Technol. Lett. 25(11), 1035–1038 (2013)
Li, Y., Lu, J., Wang, L., Yahagi, T., Okamoto, T.: Removing noise from radiological image using multineural network filter. IEEE Int. Conf. Indust. Technol. 2005, 1365–1370 (2005)
Yuanhua, G., Chunlun, H.: Functional link artificial neural networks filter for Gaussian noise. In: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). pp. 1–5 (2013)
Joseph, J., Jayaraman, S., Periyasamy, R., Simi, V.R.: An edge preservation index for evaluating nonlinear spatial restoration in MR images. Curr. Med. Imaging Rev. 13(1), 58–65 (2016)
Alilou, V.K., Yaghmaee, F.: Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recogn. Lett. 62, 24–31 (2015)
Mishra, S.K., Panda, G., Meher, S.: Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise. Int. J. Recent Trends Eng. 1(1), 413–417 (2009)
Laddi, A., Kumar, S., Sharma, S., Kumar, A.: Non-invasive jaundice detection using machine vision. IETE J. Res. 59(5), 591–596 (2013)
Das, P., Neelima, A.: An overview of approaches for content-based medical image retrieval. Int. J. Multimedia Inf. Retriev. 6(4), 271–280 (2017)
Carotenuto, R., Sabbi, G., Pappalardo, M.: Spatial resolution enhancement of ultrasound images using neural networks. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(8), 1039–1049 (2002)
Bhattacharyya, S., Pal, P., Bhowmick, S.: Binary image denoising using a quantum multilayer self organizing neural network. Appl. Soft Comput. 24, 717–729 (2014)
Chang, Y., Chang, H.: Automatic brain MR image denoising based on texture feature-based artificial neural networks. Bio-Med. Mater. Eng. 26, 1275–1282 (2015)
Ahirwal, M.K., Kumar, A., Singh, G.K.: EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(6), 1491–1504 (2013)
Naik, B., Nayak, J., Behera, H.S.: A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data. J. King Saud Univ. Comput. Inf. Sci. 30(1), 120–139 (2016)
Montana, D.J., Davis, L.: Training feed forward neural networks using genetic algorithms. IJCAI 89, 762–767 (1989)
Bashir, Z.A.: Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans. Power Syst. 24(1), 20–27 (2009)
Kumar, M., Mishra, S.K., Sahu, S.S.: Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images. Appl. Comput. Intel. Soft Comput. 2016, 1–6 (2016)
Xu, R., Ii, D.C.W., Frank, R.L.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(4), 681–692 (2007)
Mirjalili, S., Mohd Hashim, S.Z., Moradian Sardroudi, H.: Training feed forward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218(22), 11125–11137 (2012)
Zhang, D., Mabu, S., Hirasawa, K.: Noise reduction using genetic algorithm based PCNN method. In: IEEE conf. Systems Man and Cybernetics (SMC). pp. 2627–2633 (2010)
Saadi, S., Guessoum, A., Bettayeb, M.: ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess. Microsyst. 37(1), 52–64 (2013)
Kumar, M., Mishra, S.K.: Particle swarm optimization-based functional link artificial neural network for medical image denoising. In: Computational Vision and Roboticsomputational Vision and Robotics. pp. 105–111 (2015)
Kumar, M., Mishra, S.K.: Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image. Bio-Med. Mater. Eng. 28(6), 643–654 (2017)
Rao, R.V., More, K.C., Taler, J., Ocłoń, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016)
Suraj, S., Sinha, R.K., Ghosh, S.: Jaya based ANFIS for Monitoring of Two Class Motor Imagery Task. IEEE Access 4, 9273–9282 (2016)
Kumar, M., Mishra, S.K.: Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images. Multimedia Tools Appl. 77, 24405–24425 (2018)
Kumar, M., Mishra, S.K.: Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed. Res. 28(9), 4159–4164 (2017)
Ruzon, M.: RGB2Lab. MathWorks, 2009. Available:https://in.mathworks.com/matlabcentral/fileexchange/24009-rgb2lab?focused=5114484&tab=function. (Accessed: 01 Jan 2017).
Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Trans. Image Process. 21(4), 1488–1495 (2012)
Acknowledgements
We would like to thank Dr. Amit Kumar Singh, MD Radio-diagnosis and Dinesh Das, Radiologist, Medanta Abdurrazzaque Ansari Memorial Weavers Hospital, Ranchi, India for his expert comments and support.
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Kumar, M., Mishra, S.K., Choubey, D.K. et al. Multichannel heuristic learning based single layer neural network filter for mixed noise suppression from color Doppler ultrasound images. J Real-Time Image Proc 18, 1397–1408 (2021). https://doi.org/10.1007/s11554-020-01061-z
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DOI: https://doi.org/10.1007/s11554-020-01061-z