Intelligent System for Breast Cancer Prognosis using Multiwavelet Packets and Neural Network
This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
[1] CancerNet, A service of the National Cancer Institute
http://cancernet.nci.nih.gov
[2] http://www.waiu.man.ac.uk/services/MIAS
[3] P. Sajda and C. Spence. Learning Contextual Relationships in Mammograms
using a Hierarchical Pyramid Neural Network IEEE Transactions
on MedicalImaging 21 (3) (2002)
[4] B. Verma and J. Zakos, \A computer-aided diagnosis system for digital
mammograms based on fuzzy-neural and feature extraction techniques,"
Information Technology in biomedicine IEEE 5, pp. 46{54,March 2001
[5] Zheng L, Chan A. An artificial intelligent system for tumor detection in
screening mammogram. IEEE Trans Med Im2001;20(7):559-67
[6] Moti Melloul, Leo Joskowicz, Segmentation of microcalci fication in Xray
mammograms using entropy thresholding technical Report, May
2002, Hebrew University, Leibniz Center.
[7] Z. R. Yang and R. G. Harrison, \Detecting false benign in Breast cancer
diagnosis," Neural Networks, IEEE 3, pp. 655{658, July 2000.
[8] Dengler J, Behrens S, Desage JF. Segmentation of microcalcifications in
mammograms. IEEE Trans Med Image 1993; 12:634-42.
[9] Li H, Liu KJR, Lo SCB. Fractal modelling and segmentation for the
enhancement of microcalcifications in digital mammograms. IEEE
Trans Med Imag 1997; 16(6):785-98.
[10] Yoshida H, Doi K, Nishikawa RM, Giger ML,Schmidt RA.An Improved
CAD scheme using wavelet transform for detect- Ion of clustered
microcalcifications in digital mammograms. Acad Radiol 1996;3:621-7.
[11] Lado MJ, Tahoces PG, Mendez AJ, Souto M, Vidal JJ. A wavelet-based
algorithm for detecting clustered microcalcifications in digital
mammograms. Med Phys 1999; 26(7):1294-305.
[12] R. R. Coifman, Y. Meyer, and M. V. Wickerhauser, wavelet Analysis
and signal processing," in Wavelets and Their A pplications.Boston,MA:
Jones and Bartlett, 1992, pp. 153-178.
[13] J. Y. Tham, L.-X. Shen, S. L. Lee, and H. H. Tan, "A general approach
for analysis and application of discrete multiwavelet transforms," IEEE
Trans. Signal Processing, vol. 48, pp. 457-464 Feb. 2000.
[14] T. Xia and Q. Jiang, "Optimal multifilter banks: Design, related
symmetric extension transform and application to image compression,"
IEEE Trans. Signal Processing, vol. 47, pp. 1878- 1889, July 1999.
[15] S. S. Goh, Q. Jiang, and T. Xia, Construction of biorthogonal
multiwaveletsusing the lifting scheme, preprint, 1998.
[16] G. Meyer,A. Z.Averbuch, and J. O. Strömberg, "Fast adaptivewavelet
packet image compression," IEEE Trans. Image Processing, vol. 9, pp
792-800, May 2000.
[17] Xiong, K. Ramchandran, and M. T. Orchard, "Wavelet packet image
coding using space-frequency quantization," IEEE Trans. Image
Processing,vol. 7, pp. 892-898, June 1998.
[1] CancerNet, A service of the National Cancer Institute
http://cancernet.nci.nih.gov
[2] http://www.waiu.man.ac.uk/services/MIAS
[3] P. Sajda and C. Spence. Learning Contextual Relationships in Mammograms
using a Hierarchical Pyramid Neural Network IEEE Transactions
on MedicalImaging 21 (3) (2002)
[4] B. Verma and J. Zakos, \A computer-aided diagnosis system for digital
mammograms based on fuzzy-neural and feature extraction techniques,"
Information Technology in biomedicine IEEE 5, pp. 46{54,March 2001
[5] Zheng L, Chan A. An artificial intelligent system for tumor detection in
screening mammogram. IEEE Trans Med Im2001;20(7):559-67
[6] Moti Melloul, Leo Joskowicz, Segmentation of microcalci fication in Xray
mammograms using entropy thresholding technical Report, May
2002, Hebrew University, Leibniz Center.
[7] Z. R. Yang and R. G. Harrison, \Detecting false benign in Breast cancer
diagnosis," Neural Networks, IEEE 3, pp. 655{658, July 2000.
[8] Dengler J, Behrens S, Desage JF. Segmentation of microcalcifications in
mammograms. IEEE Trans Med Image 1993; 12:634-42.
[9] Li H, Liu KJR, Lo SCB. Fractal modelling and segmentation for the
enhancement of microcalcifications in digital mammograms. IEEE
Trans Med Imag 1997; 16(6):785-98.
[10] Yoshida H, Doi K, Nishikawa RM, Giger ML,Schmidt RA.An Improved
CAD scheme using wavelet transform for detect- Ion of clustered
microcalcifications in digital mammograms. Acad Radiol 1996;3:621-7.
[11] Lado MJ, Tahoces PG, Mendez AJ, Souto M, Vidal JJ. A wavelet-based
algorithm for detecting clustered microcalcifications in digital
mammograms. Med Phys 1999; 26(7):1294-305.
[12] R. R. Coifman, Y. Meyer, and M. V. Wickerhauser, wavelet Analysis
and signal processing," in Wavelets and Their A pplications.Boston,MA:
Jones and Bartlett, 1992, pp. 153-178.
[13] J. Y. Tham, L.-X. Shen, S. L. Lee, and H. H. Tan, "A general approach
for analysis and application of discrete multiwavelet transforms," IEEE
Trans. Signal Processing, vol. 48, pp. 457-464 Feb. 2000.
[14] T. Xia and Q. Jiang, "Optimal multifilter banks: Design, related
symmetric extension transform and application to image compression,"
IEEE Trans. Signal Processing, vol. 47, pp. 1878- 1889, July 1999.
[15] S. S. Goh, Q. Jiang, and T. Xia, Construction of biorthogonal
multiwaveletsusing the lifting scheme, preprint, 1998.
[16] G. Meyer,A. Z.Averbuch, and J. O. Strömberg, "Fast adaptivewavelet
packet image compression," IEEE Trans. Image Processing, vol. 9, pp
792-800, May 2000.
[17] Xiong, K. Ramchandran, and M. T. Orchard, "Wavelet packet image
coding using space-frequency quantization," IEEE Trans. Image
Processing,vol. 7, pp. 892-898, June 1998.
@article{"International Journal of Medical, Medicine and Health Sciences:62496", author = "Sepehr M.H.Jamarani and M.H.Moradi and H.Behnam and G.A.Rezai Rad", title = "Intelligent System for Breast Cancer Prognosis using Multiwavelet Packets and Neural Network", abstract = "This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.", keywords = "Breast cancer, neural networks, diagnosis,multiwavelet packet, microcalcification.", volume = "1", number = "12", pages = "651-6", }