Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Автор: G.Bharatha Sreeja, P. Rathika, D. Devaraj

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 3 vol.4, 2012 года.

Бесплатный доступ

Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.

Еще

Wavelet based Thresholding, breast cancer, mammography, window based Thresholding, segmentation.

Короткий адрес: https://sciup.org/15010689

IDR: 15010689

Список литературы Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

  • T.C.Wang, N.B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets, Medical Imaging,” IEEE Transactions, 17 , 498 -509, 1998.
  • H. Kobatake, M. Murakami, H. Takeo, and S. Nawano, “Computerized detection of malignant tumors on digital mammograms,” IEEE Trans.Med. Imag., vol. 18, no. 5, pp. 369–378, May 1999.
  • R.Mata, E.Nava,F. Sendra, “Microcalcifications detection using multi resolution methods, pattern Recognition,”2000,proceeings,15th International Conference.4,344-347,2000.
  • X. P. Zhang, “Multiscale tumor detection and segmentation in mammograms,” in Proc. IEEE Int. Symp. Biomed. Imag., pp. 213–216, Jul. 2002.
  • F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, C. Cheran, F. De Carlo, G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E. Lopez Torres, R. Magro, G. L. Masala, P. Oliva, M. Quarta, G. Raso, A. Retico, and S. Tangaro, “Mammogram segmentation by contour searching and mass lesions classification with neural network,” IEEE Trans. Nucl. Sci., vol. 53, no. 5, pp. 2827–2833, Oct. 2006.
  • Grady, L., “Random Walks for Image Segmentation,” IEEE Transactions on PAMI 28(11), 1–17, 2006.
  • Ersoy, I., Bunyak, F., Palaniappan, K., Sun, M., Forgacs, G., “Cell Spreading Analysis with Directed Edge Profile-Guided Level Set Active Contours,” In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 376–383. Springer, Heidelberg, 2008.
  • A.Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli,“Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas., vol. 57, no. 7, pp. 1422–1430, Jul. 2008.
  • Giovanni Palma, Isabelle Bloch, and Serge Muller “Spiculated Lesions and Architectural Distortions Detection in Digital Breast Tomosynthesis Datasets,” IWDM 2010, LNCS 6136, pp. 712–719, 2010.
  • S. Liu, C. F. Babbs, and E. J. Delp, “Multiresolution detection of speculated lesions in digital mammograms,” IEEE Trans. Image Process., vol. 10, no. 6, pp. 874–884, Jun. 2001.
  • G. M. te Brake and N. Karssemeijer, “Segmentation of suspicious densities in digital mammograms,” Med. Phys., vol. 28, no. 2, pp. 259–266, Feb. 2001.
  • Shengzhou Xu & Hong Liu & Enmin Song “Marker-Controlled Watershed for Lesion Segmentation in Mammograms,” J Digital Imaging 24:754–763, 2011.
  • M. Grgic et al. (Eds.): Rec. Advan. in Mult. Sig. Process. And Communication., SCI 231, pp. 631–657, 2009.
  • Umesh Adiga et al” High Throughput Analysis of Multispectral Images of Breast Cancer Tissue,” IEEE Trans on image processing, vol.15,No.8, August 2006.
  • H. S.Wu and J. Barba, “An efficient semi-automatic algorithm for cell contour extraction,” J. Microscopy, vol. 179, pp. 270–276, 1995.
  • N. H. Eltonsy, G. D. Tourassi, and A. S. Elmaghraby, “A concentric morphology model for the detection of masses in mammography,” IEEE Trans. Med. Imag., vol. 26, no. 6, pp. 880–889, Jun. 2007.
  • A. Nedzved, S. Ablameyko, and I. Pitas, “Morphological segmentation of histology cell images,” presented at the Int. Conf. Pattern Recognition, Barcelona, Spain, 2000.
  • S. Schupp, A. Elmoataz, P. Herlin, and D. Bloyet, “Mathematical morphologic segmentation dedicated to quantitative immunohistochemistry,” Anal. Quant. Cytol. Histol., vol. 23, no. 4, pp. 257–67, 2001.
  • H. S. Wu, J. Barba, and J. Gil, “A parametric fitting algorithm for segmentation of cell images,” IEEE Trans. Biomed. Eng., vol. 45, no. 3, pp. 400–407, Mar. 1998.
  • Wei Ping Li Junli, Zhao Shanxu,Lu Dongming,Chen Gang, “A Method of Detection Micro-Calcifications in Mammograms Using Wavelets and Adaptive Thresholds,” The second International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2008, pp.2361 – 2364, 2008.
  • Pradhan, S Swaroop Patra, D Nanda, P Kumar, “Adaptive Thresholding Based Image Segmentation with Uneven Lighting Condition,” the Third International Conference on Industrial and Information Systems, ICIIS, Kharagpur, December 8-10, 2008.
  • S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, Jul. 1989.
  • Grossman, A. and Morlet, J., “Decomposition of Hardy functions into square integrable wavelets of constant shape,” SIAM J. Math. Anal., Vol. 15, No. 4, pp. 723–736, 1984.
  • Garbay, “Image structure representation and processing: A discussion of some segmentation methods in cytology,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, pp. 140–146, 1986.
  • Mallat, S., “A theory for multiresolution signals decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 11, No. 7, pp. 674–693, 1989.
  • Daubechies, I., “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math., Vol. 41, No. 7, pp. 909–996, 1988.
  • Unser, M., Aldroubi, A., and Laine, A., “Special issue on wavelets in medical imaging,” IEEE transactions on medical imaging, Vol. 22, No. 3, 2003.
  • Weaver, J. B., Yansun, X., Healy, D. M., and Cromwell, L. D., “Filtering noise from images with wavelet transform,” Magn. Reson. Med., Vol. 21, No. 2, pp. 288–295, 1991.
  • X.P.Zhang and M. D. Desai, “Segmentation of bright targets using wavelets and adaptive thresholding,” IEEE Trans. Image Process., vol. 10, no. 7, pp. 1020–1030, Jul. 2001.
  • G.Kom, A.Tiedeu, and M. Kom, “Automated detection of masses in mammograms by local adaptive thresholding,” Comput. Biol. Med.,vol. 37, no. 1, pp. 37–48, Jan. 2007.
  • Maysam Shahedi B K1, Rassoul Amirfattahi1, Farah Torkamani Azar and Saeed Sadri, “Accurate breast region detection in digital mammograms using a local adaptive thresholding method,” Eight International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'07), 2007.
  • G.Kom, A. Tiedeu, and M. Kom, “Automated detection of masses in mammograms by local adaptive thresholding,” Comput. Biol. Med.,vol. 37, no. 1, pp. 37–48, Jan. 2007.
  • Brzakovic,D., Luo, X.M., Brzakovic, P., “An approach to automated detection of tumors in mammograms,” IEEE Transactions on Medical Imaging 9(3), 233–241,1990.
  • Ju Cheng Yang, Jin Wook Shin, and Dong Sun Park., “Comparing Study for Detecting Microcalcifications in Digital Mammogram Using Wavelets,” LNCS 3177, pp. 409–415, 2004.
  • A. Sarti, C. Solorzano, S. Lockett, and R. Malladi, “A geometric model for 3-D confocal image analysis,” IEEE Trans. Biomed. Eng., vol. 47, no. 12, pp. 1600–1609, Dec. 2000.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp