[1]
N. Chamadol, C. Pairojkul, N. Khuntikeo, V. Laopaiboon, W. Loilome, P. Sithithaworn, P. Yongvanit, Histological confirmation of periductal fibrosis from ultrasound diagnosis in cholangiocarcinoma patients. Journal of Hepato-Biliary-Pancreatic Sciences. (2014).
DOI: 10.1002/jhbp.64
Google Scholar
[2]
I. Elamvazuthi, M.L.B.M. Zain, K.M. Begam, Despeckling of ultrasound images of bone fracture using multiple filtering algorithms. Mathematical and Computer Modelling. 57(2013) pp.152-168.
DOI: 10.1016/j.mcm.2011.07.021
Google Scholar
[3]
D. Shao, P. Liu, D.C. Liu, Characteristic matching-based adaptive fast bilateral filter for ultrasound speckle reduction. Pattern Recognition Letters. 34(2013) pp.463-469.
DOI: 10.1016/j.patrec.2012.12.006
Google Scholar
[4]
Y. Zhang, H.D. Cheng, JiaweiTian, J. Huang, X. Tang, Fractional subpixel diffusion and fuzzy logic approach for ultrasound speckle reduction. Pattern Recognition. 43(2010) 2962-2970.
DOI: 10.1016/j.patcog.2010.02.014
Google Scholar
[5]
A. Wong, J. Scharcanski, Monte Carlo despeckling of transrectal ultrasound images of the prostate. Digital Signal Processing. 22(2012) 768-775.
DOI: 10.1016/j.dsp.2012.04.006
Google Scholar
[6]
J.L. Mateo, A. Fernández-Caballero, Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Systems with Applications. 36(2009) pp.7789-7797.
DOI: 10.1016/j.eswa.2008.11.029
Google Scholar
[7]
S. Tsantis, N. Dimitropoulos, M. Ioannidou, D. Cavouras, G. Nikiforidis, Inter-scale wavelet analysis for speckle reduction in thyroid ultrasound images. Computerized Medical Imaging and Graphics. 31(2007) 117-127.
DOI: 10.1016/j.compmedimag.2006.11.006
Google Scholar
[8]
W. Yang, S. Zhang, Y. Chen, W. Li, Y. Chen, Shape symmetry analysis of breast tumors on ultrasound images. Computers in Biology and Medicine. 39(2008) 231-238.
DOI: 10.1016/j.compbiomed.2008.12.007
Google Scholar
[9]
S. Bodziocha, M.R. Ogielab, New Approach to Gallbladder Ultrasonic Images Analysis and Lesions Recognition. Computerized Medical Imaging and Graphics. 33(2008) pp.154-170.
DOI: 10.1016/j.compmedimag.2008.11.003
Google Scholar
[10]
W. Gómez, L. Leija, W.C.A. Pereira, A.F.C. Infantosi, Semiautomatic contour detection of breast lesions in ultrasonic images with morphological operators and average radial derivative function. Physics procedia. 3(2010) 373-380.
DOI: 10.1016/j.phpro.2010.01.049
Google Scholar
[11]
S. Joo, Y.S. Yang, W.K. Moon, H.C. Kim, Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features. IEEE Transactions on Medical Imaging. 23(10) (2004) 1292-1300.
DOI: 10.1109/tmi.2004.834617
Google Scholar
[12]
Z. Yuan, F. Li, M. He, Fast Fourier transform on analysis of Portevin-Le Chatelier effect in Al 5052. Materials Science and Engineering A. 530(2011) 389-395.
DOI: 10.1016/j.msea.2011.09.101
Google Scholar
[13]
J.A. Noble, D. Boukerroui, Ultrasound Image Segmentation: A Survey. IEEE transactions on medical imaging. 25(8) (2006) 987-1010.
DOI: 10.1109/tmi.2006.877092
Google Scholar
[14]
A.M.F. Santos, R.M. d. Santos, P.M.A.C. Castro, E. Azevedo, L. Sousa, J.M.R.S. Tavares, A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images. Expert Systems with Applications. 40(2013).
DOI: 10.1016/j.eswa.2013.06.003
Google Scholar
[15]
P. Wayalun, N. Laopracha, P. Songrum, P. Wanchanthuek, Quality Evaluation for Edge Detection of Chromosome Gband Images for Segmentation. Applied Medical Informatics. 32(1) (2013) 25-32.
Google Scholar
[16]
T. Chaoqiang, Y. Lizhen, Research on collision detection algorithm Based on AABB-OBB Bounding Volume, First International Workshop on Education Technology and Computer Science, 2009, 331-333.
DOI: 10.1109/etcs.2009.82
Google Scholar