1. bookVolume 3 (2013): Edizione 4 (October 2013)
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eISSN
2449-6499
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30 Dec 2014
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4 volte all'anno
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A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images

Pubblicato online: 30 Dec 2014
Volume & Edizione: Volume 3 (2013) - Edizione 4 (October 2013)
Pagine: 265 - 276
Dettagli della rivista
License
Formato
Rivista
eISSN
2449-6499
Prima pubblicazione
30 Dec 2014
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese

[1] R. Chang, W. Wu, W. Moon, and D. Chen. Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Research and Treatment, 89(2):179-185, 2005.10.1007/s10549-004-2043-zSearch in Google Scholar

[2] D. Chen, Y. Huang, and S. Lin. Computer-aided diagnosis with textural features for breast lesions in sonograms. Computerized Medical Imaging and Graphics, 35:220-226, 2011.10.1016/j.compmedimag.2010.11.003Search in Google Scholar

[3] Z. Dokur and T. Olmez. Segmentation of ultrasound images by using a hybrid neural network. Pattern Recognition Letters, 23(14):1825-1836, 2002.10.1016/S0167-8655(02)00155-1Search in Google Scholar

[4] R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley, New York, 2001.Search in Google Scholar

[5] D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun. Understanding deep architectures using a recursive convolutional network. ArXiv e-prints, 2013.Search in Google Scholar

[6] R. Entrekin, P. Jackson, J. Jago, and B. Porter.Real time spatial compound imaging in breast ultrasound: technology and early clinical experience. Medicamundi, 43(3), 1990.Search in Google Scholar

[7] W. Gomez, W. Pereira, and A. Infantosi. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Transactions on Medical Imaging, 31, 2012.10.1109/TMI.2012.220639822759441Search in Google Scholar

[8] S. Gupta, R. Chauhan, and S. Sexena. Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Medical and Biological Engineering and Computing, 43:189-195, 2005.10.1007/BF0234595315865126Search in Google Scholar

[9] F. J. Huang and Y. LeCun. Large-scale learning with svm and convolutional nets for generic object categorization. Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1:284-291, 2006.Search in Google Scholar

[10] K. Ikedo, Y., F. D., T. Hara, H. Fujita, E. Takada, T. Endo, and T. Morita. Computerized mass detection in whole breast ultrasound. Medical Imaging, 6514, 2007.Search in Google Scholar

[11] A. Katouzian, E. Angelini, S. Carlier, J. Suri, N. Navab, and A. Laine. A state-of-the-art review on segmentation algorithms in intravascular ultrasound (ivus) images. IEEE Transactions on Information Technology in Biomedicine, 16(5):823-834, 2012.10.1109/TITB.2012.218940822389156Search in Google Scholar

[12] L. Kuncheva. Combining Pattern Classifiers. Wiley-Interscience, Hoboken, New Jersey, 2004.10.1002/0471660264Search in Google Scholar

[13] F. Lauera, C. Suen, and G. Bloch. A trainable feature extractor for handwritten digit recognition. Pattern Recognition, 40(6):1816-1824, 2007.10.1016/j.patcog.2006.10.011Search in Google Scholar

[14] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a backpropagation network. Advances in Neural Information Processing Systems, pages 396-404, 1990.Search in Google Scholar

[15] Y. LeCun, L. Bottou, Y. Bengio, and H. P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.10.1109/5.726791Search in Google Scholar

[16] B. Liu, H. Cheng, J. Huang, J. Tian, J. Liu, and X. Tang. Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance. Ultrasound in Medicine & Biology, 35(8):1309-1324, 2009.10.1016/j.ultrasmedbio.2008.12.00719481332Search in Google Scholar

[17] A. Madabhushi and D. Metaxas. Combining low- , high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Transactions on Medical Imaging, 22(2):155-169, 2003.10.1109/TMI.2002.80836412715992Search in Google Scholar

[18] M. Mancas, B. Gosselin, and B. Macq. Segmentation using a region growing thresholding. 4th Image Processing: Algorithms and Systems, 56:388-398, 2005.10.1117/12.587995Search in Google Scholar

[19] Y. Meyer. Wavelets and Operators. Cambridge University Press, Cambridge, 1993.10.1017/CBO9780511623820Search in Google Scholar

[20] W. Moon, C. Lo, J. Chang, C. Huang, J. Chen, and C. R. Computer-aided classification of breast masses using speckle features of automated breast ultrasound images. Medical Physics, 39, 2012.10.1118/1.475480123039681Search in Google Scholar

[21] W. Moon, Y. Shen, M. Bae, C. Huang, and J. Chen. Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. Pattern Recognition, 32(7):1191-1200, 2013.10.1109/TMI.2012.223040323232413Search in Google Scholar

[22] F. Sahba, M. Tizhoosh, and M. Salma. Segmentation of prostate boundaries using regional contrast enhancement. IEEE International Conference on Image Processing (ICIP), 2:1266-1269, 2005.10.1109/ICIP.2005.1530293Search in Google Scholar

[23] J. Shan, H. Cheng, and Y. Wang. A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering. Medical Physics, 39(9):5669-5682, 2012.10.1118/1.474727122957633Search in Google Scholar

[24] A. Sohail, P. Bhattacharya, S. Mudur, and S. Krishnamurthy. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. Pattern Recognition and Image Analysis, 6669:176-183, 2011.10.1007/978-3-642-21257-4_22Search in Google Scholar

[25] U. Techavipoo, Q. Chen, T. Varghese, A. Zagzebski, and E. Madsen. Noise reduction using spatial-angular compounding for elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 51(5):510 - 520, 2004.10.1109/TUFFC.2004.1308687Search in Google Scholar

[26] G. Treece, A. Gee, and R. Prager. Ultrasound compounding with automatic attenuation compensation using paired angle scans. Ultrasound in Medicine and Biology, 33(4):630-642, 2007.10.1016/j.ultrasmedbio.2006.09.01217320269Search in Google Scholar

[27] H. Tu, J. Zagzebski, A. Gerig, Q. Chen, E. Madsen, and T. Hall. Optimization of angular and frequency compounding in ultrasonic attenuation estimation. Journal of the Acoustical Society of America, 117(5):3307-3318, 2005.10.1121/1.1879212Search in Google Scholar

[28] V. Ulagamuthalvi and D. Sridharan. Automatic identification of ultrasound liver cancer tumor using support vector machine. International Conference on Emerging Trends in Computer and Electronics Engineering, pages 41-43, 2012.Search in Google Scholar

[29] H. Yang, C. Chang, S. Huang, and P. Li. Correlations among acoustic, texture and morphological features for breast ultrasound cad. Ultrasound Imaging, 30(4):228-236, 2008.10.1177/016173460803000404Search in Google Scholar

[30] M. Yap. A novel algorithm for initial lesion detection in ultrasound breast images. Journal of Applied Clinical Medical Physics, 9(4), 2008.10.1120/jacmp.v9i4.2741Search in Google Scholar

[31] L. Zadeh. Fuzzy sets. Information and Control, 8(3):338-353, 1965.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

[32] L. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8:199-249, 1975.10.1016/0020-0255(75)90036-5Search in Google Scholar

[33] M. Zhang. Novel Approaches to Image Segmentation Based on Neutrosophic Logic. PhD thesis, Utah State University, 2010. Search in Google Scholar

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