Abstract
In this research, a new method for automatic detection of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, the best de-noising technique from among several considered is selected, a new segmentation based on fuzzy logic is proposed and detection of lesions based on morphological features and texture features is considered. We also consider correlation among ultrasound images taken from different angles and use it to improve detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yap, M.H.: A novel algorithm for initial lesion detection in ultrasound breast images. Journal of Applied Clinical Medical Physics 9(4) (2008)
Ikedo, Y., Fukuoka, D., Hara, J., Fujita, H., Takada, E., Endo, T., Morita, T.: Computerized mass detection in whole breast ultrasound images: Reduction of false positives using bilateral subtraction technique. In: Medical Imaging. Proc. of SPIE, vol. 6514, pp. 1–10 (2007)
Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Takada, E., Endo, T., Morita, T.: Development of a fully automatic scheme for detection of masses in whole breast ultrasound images. Medical Physics 34, 4378–4388 (2007)
Moon, W.K., Lo, C., Chang, J., Huang, C., Chen, J., Chang, R.: Computer-aided classification of breast masses using speckle features of automated breast ultrasound images. Medical Physics. 39 (2012)
Gupta, S., Chauhan, R., Sexena, S.: Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Medical and Biological Engineering and Computing 43, 189–195 (2005)
Benes, R., Riha, K.: Noise Reduction in Medical Ultrasound Images. Elektrorevue 2(3) (2011)
Roy, S., Sinha, N., Sen, A.: A New Hybrid Image Denoising Method. International Journal of Information Technology and Knowledge Management 2(2), 491–497 (2010)
Nicolae, M.C., Moraru, L., Gogu, A.: Speckle noise reduction of ultrasound images. Medical Ultrasonography an International Journal of Clinical Imaging 11, 50–51 (2009)
Joo, S., Moon, W.K., Kim, H.C.: Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network. Medicine and Biology Society 2, 1397–1400 (2004)
Yeh, C.K., Chen, Y.S., Fan, W.C., Liao, Y.Y.: A disk expansion segmentatoin method for ultrasonic breast lesions. Pattern Recognition 42(5), 596–606 (2009)
Moon, W.K., Shen, Y.W., Bae, M.S., Huang, C.H., Chen, J.H.: Computer-Aided Tumor Detection Based on Multi-scale Blob Detection Algorithm in Automated Breast Ultrasound Images. IEEE Transaction on Medical Imaging 32(7), 1191–1200 (2013)
Chang, R.F., Wu, W.J., Moon, W.K., Chen, D.R.: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Research and Treatment 89(2), 179–185 (2005)
Sennett, C.A., Giger, M.L.: Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound 28(1), 122–128 (2009)
Lihua, L., Jiangli, L., Deyu, L., Tianfu, W.: Segmentation of medical ultrasound image based on markov random field. Bioinformatics and Biomedical Engineering 968–971 (2007)
Deka, B., Ghosh, D.: Ultrasound image segmentation using watersheds and region merging. In IET International Conference on Visual Information Engineering, Banglore, pp. 110–115(2006)
Pereira, W.C.A., Infantosi, A.F.C.: Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound. IEEE Transactions on Medical Imaging 31(10), 1889–1899 (2012)
Wu, W.J., Kyung Moon, W.: Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Academic Radiology 15(7), 873–880 (2008)
Ramana Reddy, B.V., Suresh, A., Radhika Mani, M., Vijaya Kumar, V.: Classification of Textures Based on Features Extracted from Preprocessing Images on Random Windows. International Journal of Advanced Science and Technology 9 (2009)
Tamilselvi, P.R., Thangaraj, P.: Improved Gabor filter for extracting texture edge features in US kidney images. Modern Applied Science 4 (2010)
Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Transactions on Neural Networks 13(2), 464–471 (2002)
Lui, B., Cheng, H., Huang, J., Tian, J., Tang, X., Liu, J.: Probability density difference-based active contour for ultrasound image segmentation. Journal Pattern Recognition 43(6), 2028–2042 (2010)
Selvan, S., Kavitha, M., Shenbagadevi, S., Suresh, K.: Feature Extraction for Characterization of Breast Lesions in Ultrasound Elastography and Echography. Journal of Computer Science 16, 67–74 (2010)
Kotropoulos, C., Pitas, I.: Segmentation of ultrasonic images using support vector machines. Pattern Recognition Letters 24(4), 715–725 (2004)
Zadeh, L.A.: The concept of linguistic variable and its application to approximate reasoning - I. Information Science 8, 199–249 (1975)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Sahba, F., Tizhoosh, M.R., Salma, M.M.A.: Segmentation of prostate boundaries using regional contrast enhancement. In: The IEEE International Conference on Image Processing (ICIP), vol. 2, pp. 1266–1269 (2005)
Haihui, W., Yanli, W., Tongzhou, T., Miao, W., Mingpeng, W.: Images segmentation method on comparison of feature extractoin techniques. In: 2nd International Workshop on Intelligent Systems and Applications, pp. 1–4 (2010)
Dokur, Z., Olmez, T.: Segmentation of ultrasound images by using a hybrid neural network. Pattern Recognition Letters 23(14), 1825–1836 (2002)
Sarti, A., Corsi, C., Mazzini, E., Lamberti, C.: Maximum likelihood segmentation with Rayleigh distribution of ultrasound images. Computers in Cardiology 31, 329–332 (2004)
Zhang, H., Fritts, J.E., Goldman, S.A.: A fast texture feature extraction method for region-based image segmentation. Image and Video Communications and Processing 5685, 957–968 (2005)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Saini, K., Dewal, M.L., Rohit, M.: Ultrasound Imaging and Image Segmentation in the area of Ultrasound: A Review. International Journal of Advanced Science and Technology 24 (2010)
Sohail, A.S.M., Bhattacharya, P., Mudur, S.P., Krishnamurthy, S.: Classification of ultrasound medical images using distance based feature selection and fuzzy-SVM. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 176–183. Springer, Heidelberg (2011)
Burrell, L.S., Smart, O.L., Georgoulas, G., Marsh, E., Vachtsevanos, G.J.: Evaluation of Feature Selection Techniques for Analysis of Functional MRI and EEG. In: 2007 International Conference on Data Mining, DMIN, Las Vegas (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Karimi, B., Krzyżak, A. (2014). Computer-Aided System for Automatic Classification of Suspicious Lesions in Breast Ultrasound Images. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-07176-3_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07175-6
Online ISBN: 978-3-319-07176-3
eBook Packages: Computer ScienceComputer Science (R0)