超音波影像含有假影和斑紋，使特徵和組織邊界難以觀察。在此碩論中，我們提出基於多窗口局部變異數特徵偵測之超音波影像銳化與斑紋去除技術，來主動強化超音波影像的邊界和特徵以及消除斑紋雜訊。影像銳化是由加強影像中特徵的高頻訊息，使其影像特徵看似更加銳利。然而，超音波影像中的斑紋也是高頻訊息，往往會被誤認為特徵而被錯誤的加強。因此，我們提出一個能在斑紋雜訊下偵測超音波特徵的方法來進行影像銳化 – 多窗口局部變異數特徵偵測法。此方法利用超音波斑紋的統計特性 – 均勻非特徵的斑紋雜訊區域其機率密度函數的變異數是固定的，藉由局部變異數的值來偵測特徵。此方法在計算局部變異數時，使用多個不同大小的窗口來偵測，再將所有偵測的特徵融合為一個壓抑斑點假特徵的特徵影像，使得接下來的影像銳化較文獻中常見超音波影像銳化技術能更有效地銳化超音波影像中實際的特徵及邊界。此外，基於所提出的多窗口局部變異數特徵偵測，我們也提出一個適應性非局部均值濾波的方法來消除超音波影像中的斑紋雜訊。基於偵測所得的特徵適應性地調整影像中各個位置的濾波強度。特徵處就給予較低的濾波強度，使其在消除斑紋雜訊的同時亦保留超音波影像中重要的特徵。結果顯示所提出適應性非局部均值濾波的方法較目前文獻中用於斑紋雜訊消除的非局部均值濾波技術可以更平衡地取得去斑紋雜訊效果。

Ultrasound images suffer from speckle noises and several artifacts that degrade image contrast and cause difficulties in observing borders and features. In this thesis, we propose a novel image sharpening algorithm and a new speckle reduction technique using multi-window local variance detected features to actively enhances edges and borders and suppress speckles in ultrasound images. Image sharpening generally is done by amplifying the high-frequency components representing features in the image. However, ultrasound images suffer speckle noises which are also with high frequencies and thus tends to be undesirably sharpened. In order not to sharpen speckle noises, a feature mask is needed to adaptively adjust the enhancing strength. Here, we present a multiscale local variance (MLV) feature detection technique featuring robust feature detection under speckle noises for ultrasound images. Based on the constant variance characteristic of Fisher-Tippet distribution in log-compressed ultrasound images, MLV use local variances to detect features. MLV calculates the local variance using a multi-window framework, in which several window sizes are used to detect features of different scales, then being combined together to obtain one robust and fine feature map. Such a robust and fine feature map enables more effective image sharpening than the techniques commonly used for ultrasound image enhancement in the literature. The efficacy of the proposed MLV based image sharpening algorithm is tested on simulation and real ultrasound B-mode images, showing improvements in objective metrics and subjective evaluations. In addition, based on MLV feature detection, we propose an adaptive non-local means (ANLM) filter for ultrasound image speckle reduction. ANLM uses spatial-varying smoothing parameters that are determined by the MLV detected features. Feature regions are given weaker smoothing strength for feature preservation. Results show that ANLM generates balanced and optimal speckle reduction results than the conventional non-local means filtering.

[1] S. H. C. Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 419 – 428, 2012. 1
[2] I. Thomassin-Naggara, A. Tardivon, and J. Chopier, “Standardized diagnosis and reporting of breast cancer,” Diagnostic and Interventional Imaging, vol. 95, no. 7, pp. 759 – 766, 2014. Interventional radiology in oncology. 1
[3] L. H. Breivik, S. R. Snare, E. N. Steen, and A. H. S. Solberg, “Real-time nonlocal means-based despeckling,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 64, pp. 959–977, June 2017. 2, 9, 21
[4] S. K. Narayanan and R. S. D. Wahidabanu, “A view on despeckling in ultrasound imaging,” sep 2009. 2
[5] M. Tsubai, T. Nishimura, A. Sasaki, T. Mitake, and S. Umemura, “A study of morphological edge enhancement by double structuring element for ultrasound b-mode images [biomedical imaging],” in IEEE Ultrasonics Symposium, 2004, vol. 2, pp. 1437–1440 Vol.2, 2004. 2
[6] E. Supriyanto, N. S. A. Zulkifli, M. M. Baigi, N. Humaimi, and B. Rosidi, “Abnormal tissue detection of breast ultrasound image using combination of morphological technique,” in Proceedings of the 15th WSEAS International Conference on Computers, (Stevens Point, Wisconsin, USA), p. 234–239, World Scientific and Engineering Academy and Society (WSEAS), 2011. 2
[7] S. Anand, R. S. S. Kumari], T. Thivya, and S. Jeeva, “Sharpening enhancement of ultrasound images using contourlet transform,” Optik, vol. 124, no. 21, pp. 4789 – 4792, 2013. 2
[8] K. Panetta, Y. Zhou, S. Agaian, and H. Jia, “Nonlinear unsharp masking for mammogram enhancement,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 6, pp. 918–928, 2011. 2
[9] A. J.N, “A review on the image sharpening algorithms using unsharp masking,” IJESC, vol. 6, pp. 8729–8733, 08 2016. 3, 18
[10] A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 60–65 vol. 2, June 2005. 5, 8
[11] P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Transactions on Image Processing, vol. 18, pp. 2221–2229, Oct 2009. 8, 9, 35
[12] Yongjian Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Transactions on Image Processing, vol. 11, no. 11, pp. 1260–1270, 2002. 8
[13] C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound image despeckling using stochastic distance-based bm3d,” IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2632–2643, 2017. 8
[14] Y. S. Kim and J. B. Ra, “Improvement of ultrasound image based on wavelet transform: speckle reduction and edge enhancement,” in Medical Imaging 2005: Image Processing (J. M. Fitzpatrick and J. M. Reinhardt, eds.), vol. 5747, pp. 1085 – 1092, International Society for Optics and Photonics, SPIE, 2005. 8
[15] Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Processing, vol. 103, pp. 201 – 213, 2014. Image Restoration and Enhancement: Recent Advances and Applications. 8, 9
[16] A. Perperidis, “Postprocessing approaches for the improvement of cardiac ultrasound b-mode images: A review,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 63, no. 3, pp. 470–485, 2016. 8
[17] A. Foi and G. Boracchi, “Foveated nonlocal self-similarity,” Int. J. Comput. Vision, vol. 120, p. 78–110, Oct. 2016. 9
[18] X. Wang, S. Shen, G. Shi, Y. Xu, and P. Zhang, “Iterative non-local means filter for salt and pepper noise removal,” Journal of Visual Communication and Image Representation, vol. 38, pp. 440 – 450, 2016. 9
[19] J. V. Manjón, P. Coupé, L. Martí-Bonmatí, D. L. Collins, and M. Robles, “Adaptive non-local means denoising of mr images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192–203, 2010. 9
[20] S. Shen, X. Fang, and C. Wang, “Adaptive non-local means filtering for image deblocking,” in 2011 4th International Congress on Image and Signal Processing, vol. 2, pp. 656–659, Oct 2011. 10
[21] L. Yang, R. Parton, G. Ball, Z. Qiu, A. H. Greenaway, I. Davis, and W. Lu, “An adaptive non-local means filter for denoising live-cell images and improving particle detection,” Journal of Structural Biology, vol. 172, no. 3, pp. 233 – 243, 2010. 10
[22] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679–698, 1986. 10
[23] M. Nikolic, E. Tuba, and M. Tuba, “Edge detection in medical ultrasound images using adjusted canny edge detection algorithm,” in 2016 24th Telecommunications Forum (TELFOR), pp. 1–4, 2016. 10
[24] H. Y. Chai, L. K. Wee, and E. Supriyanto, “Edge detection in ultrasound images using speckle reducing anisotropic diffusion in canny edge detector framework,” in Proceedings of the 15th WSEAS International Conference on Systems, (Stevens Point, Wisconsin, USA), p. 226–231, World Scientific and Engineering Academy and Society (WSEAS), 2011. 10
[25] Y. Zheng, Y. Zhou, H. Zhou, and X. Gong, “Ultrasound image edge detection based on a novel multiplicative gradient and canny operator,” Ultrasonic Imaging, vol. 37, no. 3, pp. 238–250, 2015. PMID: 25315657. 10
[26] A. B. Hamza, “Nonextensive information-theoretic measure for image edge detection,” Journal of Electronic Imaging, vol. 15, no. 1, pp. 1 – 8, 2006. 11
[27] G. Slabaugh, G. Unal, and T. Chang, “Information-theoretic feature detection in ultrasound images,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2638–2642, Aug 2006. 11, 23
[28] M. S. Prieto and A. R. Allen, “A similarity metric for edge images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1265– 1273, 2003. 11
[29] R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, “Statistics of speckle in ultrasound b-scans,” IEEE Transactions on Sonics and Ultrasonics, vol. 30, no. 3, pp. 156–163, 1983. 11, 13
[30] P. Mohana Shankar, “A general statistical model for ultrasonic backscattering from tissues,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 47, no. 3, pp. 727–736, 2000. 11
[31] B. Wang and D. C. Liu, “A novel edge enhancement method for ultrasound imaging,” in 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 2414–2417, May 2008. 12, 22
[32] P. Singh, R. Mukundan, and R. De Ryke, “Feature enhancement of medical ultrasound scans using multifractal measures,” in 2019 IEEE International Conference on Signals and Systems (ICSigSys), pp. 85–91, 2019. 12
[33] V. Dutt and J. F. Greenleaf, “Statistics of the logcompressed echo envelope,” The Journal of the Acoustical Society of America, vol. 99, no. 6, pp. 3817–3825, 1996. 14
[34] D. Kaplan and Q. Ma, “On the statistical characteristics of log-compressed Rayleigh signals: Theoretical formulation and experimental results,” Acoustical Society of America Journal, vol. 95, pp. 1396–1400, Mar. 1994. 14
[35] 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, pp. 1847–1860, July 2010. 16
[36] S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. T. H. Romeny, and J. B. Zimmerman, “Adaptive histogram equalization and its variations,” Comput. Vision Graph. Image Process., vol. 39, p. 355–368, Sept. 1987. 1
[37] M. D. Heath, S. Sarkar, T. Sanocki, and K. W. Bowyer, “A robust visual method for assessing the relative performance of edge-detection algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1338– 1359, 1997. 21
[38] J. Jensen, “Field: A program for simulating ultrasound systems,” Medical & Biological Engineering & Computing, vol. 34, no. sup. 1, pp. 351–353, 1997. 21
[39] Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, pp. 600–612, April 2004. 23
[40] O. Huang, W. Long, N. Bottenus, M. Lerendegui, G. E. Trahey, S. Farsiu, and M. L. Palmeri, “Mimicknet, mimicking clinical image post-processing under black-box constraints,” IEEE Transactions on Medical Imaging, pp. 1–1, 2020. 24, 32
[41] M. Tsubai, N. Mitoda, O. Fukuda, and N. Ueno, “An implementation of image sharpening based on morphological operations for ubiquitous echo,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2742–2745, 2006. 35
[42] S. Leclerc, E. Smistad, J. Pedrosa, A. Østvik, F. Cervenansky, F. Espinosa, T. Espeland, E. A. R. Berg, P. Jodoin, T. Grenier, C. Lartizien, J. D’hooge, L. Lovstakken, and O. Bernard, “Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,” IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198–2210, 2019. 35
[43] B. M. Asl and A. Mahloojifar, “Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 9, pp. 1923–1931, 2009. 41
[44] G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Transactions on Medical Imaging, vol. 34, no. 4, pp. 940–949, 2015. 41
[45] S. K. Jespersen, J. E. Wilhjelm, and H. Sillesen, “Multi-angle compound imaging,” Ultrasonic Imaging, vol. 20, no. 2, pp. 81–102, 1998. PMID: 9691367. 41
[46] P. A. Magnin, O. T. von Ramm, and F. L. Thurstone, “Frequency compounding for speckle contrast reduction in phased array images,” Ultrasonic Imaging, vol. 4, no. 3, pp. 267–281, 1982. PMID: 6889772. 41