Reconstruction of three-dimensional breast-tumor model using multispectral gradient vector flow snake method

 

Sheng-Chih Yang, Cheng-Yi Yu, Cheng-Jian Lin, Hsueh-Yi Lin, Chi-Yuan Lin*

 

Department of Computer Science and Information Engineering, National Chin Yi University of Technology, Taichung, Taiwan. *Corresponding author. E-mail address: chiyuan@ncut.edu.tw

 

Abstract

In this study, we have proposed a three-dimensional (3D) model reconstruction system for breast tumors. The proposed system can establish an accurate 3D model of tumors, which will serve as a diagnostic reference for physicians and also address the shortcomings of the traditional breast needle localization method and other localization methods reported in previous studies. This developed system uses multispectral breast magnetic resonance images as input and detects the contour of the tumor in different sections using an active contour method - multispectral gradient vector flow snake (MGVFS) method. Thus, the system constructs a 3D model of only the tumor is contained in a breast surface model and excludes other tissues. Since the accuracy of the reconstructed 3D model depends on the accuracy of the tumor contour detection, for confirming the results obtained with the MGVFS method, we conducted experiments to evaluate its accuracy in contour detection, and compared the results with those traditional contour detection methods. Our results demonstrate that the MGVFS method has the highest accuracy in contour detection, with a correct contour detection rate as high as 99.79%.

Keywords: Three-dimensional model reconstruction; Contour detection; Multispectral gradient vector flow snake; Breast magnetic resonance image; Breast needle localization.

 

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Acknowledgments

The work was supported by the National Science Council, Taiwan, under the Grant Nos. NSC 101-2221-E-167-036 and NSC 102-2221-E-167-030. The Authors would like to thank Mr. Cheng-Yan Wang for providing his experiences in MATLAB programing of 3D model.

 

References

Beucher, S. (1991). The Watershed Transformation Applied to Image Segmentations (pp. 299-314). Conference on Signal and Image Processing in Microscopy and Microanalysis.         [ Links ]

Central Vermont Medical Center (2015). Breast Needle Localization. Retrieved from: http://www.cvmc.org/our-services/cancer-care/surgical-services/breast-needle-localization        [ Links ]

Chung, P.C., & Yang, S.C. (2001). A computer-aided system for 3D localization of clustered microcalcifications in mam-mograms. National Science Council research report, Taiwan. Project No.: NSC 90-2213-E-006-092.         [ Links ]

Davatzikos, C., & Prince, J.L. (1995). An active contour model for mapping the cortex. IEEE T. Med. Imaging. 14, 65-80.         [ Links ]

Elbasi, E. (2012). Robust MPEG Watermarking in DWT Four Bands. Journal of Applied Research and Technology, 10, 87-93.         [ Links ]

Farrand, W., & Harsanyi, J.C. (1997). Mapping the distribution of mine tailing in the Coeur d'Alene river valley, Idaho, through the use of constrained energy minimization technique. Remote Sens. Environ., 59, 64-76.         [ Links ]

Haddon, J., & Boyce J. (1990). Image segmentation by unifying region and boundary information. IEEE T. Pattern Anal., 12, 929-948.         [ Links ]

Huang, C.R., Chung, P.C., Lee, T.Y., Yang, S.C., & Lee, S.K. (2006). Reconstruction and rendering of microcalcifications from two mammogram views by modified projective grid space (MPGS). Comput. Med. Imag. Grap., 30, 123-133.         [ Links ]

Jinda-apiraksa, A., Ongy, S.H., Hiew, L.T., Foong, K.W.C., & Kondo, T. (2009). A segmentation technique for maxillary sinus using the 3-D level set method (pp. 1-6). Tencon 2009-2009 IEEE Region 10 Conference.         [ Links ]

Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contour models. Int. J. Comput. Vision, 1, 321-331.         [ Links ]

Mamun, M., Al-Kadi M., & Marufuzzaman, M. (2013). Effectiveness of wavelet denoising on electroencephalogram signals. Journal of Applied Research and Technology, 11, 156-160.         [ Links ]

Montefusco, L.B., Lazzaro, D., Papi, S., & Guerrini, C. (2011). A fast compressed sensing approach to 3D MR image reconstruction. IEEE T. Med. Imaging, 30, 1064-1075.         [ Links ]

Resmini, R.S., Kappus, M.E., Aldrich, W.S., Harsanyi, J.C., & Anderson M. (1997). Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A. Int. J. Remote Sens., 18, 1553-1570.         [ Links ]

Wang, C.M., Chen, C.C., Chung, Y.N., Yang, S.C., Chung, P.C., Yang, C.W., & Chang, C.I. (2003). Detection of spectral signatures in multispectral MR images for classification. IEEE T. Med. Imaging, 12, 50-61.         [ Links ]

Xu, C., & Prince, J.L. (1997). Gradient vector flow: a new external force for snake. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 66-71.         [ Links ]

Yang, S.C., Hsu, H.H., Hsu, G.C., Chung, P.C., Guo, S.M., Lo, C.S., & Chang, C.I. (2005). 3D localization of clustered microcalcifications using cranio-caudal and medio-lateral oblique views. Comput. Med. Imag. Grap., 29, 521-532.         [ Links ]

Zhang, Y., Zhou, Y., Yang, X., Tang, P., Qiu, Q., Liang, Y., & Jiang, J. (2013). Thin slice three dimentional (3D) reconstruction versus CT 3D reconstruction of human breast cancer. Indian J. Med. Res., 37, 57-62.         [ Links ]