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Rate-Distortion Based Segmentation of Tumor Region in an Breast Ultrasound Volume Image  

Kwak, Jong-In (Department of Electronic Engineering, Kyungpook National University)
Kim, Sang-Hyun (Department of Multimedia Engineering, Youngsan University)
Kim, Nam-Chul (Department of Electronic Engineering, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.42, no.5, 2005 , pp. 51-58 More about this Journal
Abstract
This paper proposes an efficient algorithm for extracting a tumor region from an breast ultrasound volume image by using rate-distortion (R-D) based seeded region growing. In the proposed algorithm the rate and the distortion represent the roughness of the contour and the dissimilarity of pixels in a region, respectively. Staring from an initial seed region set in each cutting plane of a volume, a pair of the seed region and one of adjacent regions whose R-D cost is minimal is searched and then they are merged into a new updated seed region. This procedure is recursively performed until the averaged R-D cost values per the number of contour pixels in the seed region becomes maxim. As a result, the final seed region has good pixel homogeneity and a much smooth contour. Finally, the tumor volume is extracted using the contours of the final seed regions in all the cutting planes. Experimental results show that the averaged error rate of the proposed method is shown to be below 4%.
Keywords
초음파 볼륨;흉부 종양;영상 분할;율왜곡;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 M. Martin and E. Rodriguez, 'Energy functions for the segmentation of ultrasound volume data using active rays,' in Proc. IEEE Int. Conf. on ICASSP'00, vol. 4, pp. 2274-2277, 2000   DOI
2 S. Ogawa and K. Itoh, 'Three dimensional ultrasonic imaging for diagnosis of breast tumor,' IEEE Ultrasonics Symp., pp. 1677-1680, 1998   DOI
3 J. U. Quistgaard, 'Signal acquisition and processing in medical diagnostic ultrasound,' IEEE Signal Processing Magazine, vol. 14, pp. 67-74, 1997   DOI   ScienceOn
4 C. W. Lim, N. C. Kim, S. C. Jun, and C. S. Jung, 'Rate-distortion based image segmentation using recursive merging,' IEEE Trans. Circuits Syst. Video Technology, vol. 10, no. 7, pp. 1121-1134, Oct. 2000   과학기술학회마을
5 P. N. T. Wells, 'Current status and future technical advances of ultrasonic imaging,' IEEE Engineering in Medicine and Biology Magazine, pp. 14-20, 2000   DOI   ScienceOn
6 T. Lango, T. Lie, O. Husby, and J. Hokland, 'Bayesian 2-D deconvolution: Effect of using spatially invariant ultrasound point spread function,' IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 48, no. 1, Jan. 2001   DOI   ScienceOn
7 G. Xiao, M. Brady, A. Noble, and Y. Zhang, 'Segmentation of ultrasound B-Mode images with intensity inhomogeneity correction,' IEEE Trans. on Medical Imaging, vol.21, no.1, pp. 48 - 57, Jan. 2002   DOI   ScienceOn
8 C. S. Choi, H. harashima, and T. Takebe, 'Analysis and synthesis of facial expressions in knowledge-based coding of facial image sequences.' Proc. ICASSP, pp. 2737-2740, 1991   DOI
9 A. Watt, Fundamental of Three-Dimensional Computer Graphics, Addison Wesley, 1990
10 J. I. Kwak, M. N. Jung, S. H. Kim, and N. C. Kim, '3D Segmentation of Breast Tumor in Ultrasound Images,' Medical Imaging 2003 Ultrasonic Imaging and Signal Processing, vol. 4, no. 26, pp. 193-200, Feb. 2003   DOI
11 A. Wahle, G. P. M. Prause, S. C. DeJong, and M. Sonka, 'Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with biplane angiography-methods and validation,' IEEE Trans. on Medical Imaging, vol. 18, pp. 686-699, Aug. 1999   DOI   ScienceOn
12 D. F. Leotta, J. F. Primozich, K. W. Beach, R. O. Bergelin, K. D. Gibson, and D. E. Strandness, Jr., 'Cross-sectional area changes in peripheral vein grafts monitored by three-dimensional ultrasound imaging,' IEEE Ultrasonics Symp., vol. 2, pp. 1865-1868, Oct. 2000   DOI
13 R. Steven and L. Horowitz, 'Picture segmentation by a tree traversal algorithm,' Journal of ACM, vol. 23, no. 2, pp. 368-388, Apr. 1976   DOI   ScienceOn
14 A. Madabhushi and D. N. Metaxas, 'Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions,' IEEE Trans. on Medical Imaging, vol.22, no.2, pp. 155-169, Feb. 2003   DOI   ScienceOn
15 R. Entrekin, P. Jackson, J. R. jago, and B. A. Porter, 'Real time spatial compound imaging in breast ultrasound: technology and early clinical experience,' Medica Mundi, vol. 43, pp. 35-43, 1999
16 X. Hao, C. Bruce, C. Pislaru, and J. Greenleaf, 'Segmenting high-frequency intracardiac ultrasound images of myocardium into infarcted, ischemic, and normal regions,' IEEE Trans. Med. Imag., vol. 20, pp. 1373-1383, Dec. 2001   DOI   ScienceOn
17 P. N. T. Wells, 'Absorption and dispersion of ultrasound in biological tissue,' Ultrasound in Medicine and Biology, pp. 369-376, 1975   DOI   ScienceOn
18 Z. Lin, J. Jin, and H. Talbot, 'Unseeded region growing for 3D image segmentation,' ACM International Conference Proceeding Series, Selected papers from the Pan-Sydney Workshop on Visualization, vol. 2, pp. 31-37, Dec. 2000
19 A. Krivanek and M. Sonka, 'Ovarian ultrasound image analysis: follicle segmentation,' IEEE Trans. Medical Imaging, vol. 17, no. 6, pp. 935-944, Dec. 1998   DOI   ScienceOn