대한전기학회논문지:시스템및제어부문D (The Transactions of the Korean Institute of Electrical Engineers D)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

Independent Component Analysis를 이용한 의료영상의 자동 분할에 관한 연구

A Study of Automatic Medical Image Segmentation using Independent Component Analysis

  • 발행 : 2003.01.01
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초록

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

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참고문헌

  1. G. Wang, M. W. Vannier, M. W. Skinner, W. A. Kalender, A. Polacin and D. R. Ketten, 'Unwrapping cochlear implants by spiral CT,' IEEE Transactions of Biomedical Engineering, vol.43, no.9, pp.891-900, 1996 https://doi.org/10.1109/10.532123
  2. H. L. Seldon, 'Three-dimensional reconstruction of temporal bone from computed tomographic scans on a personal computer,' Arch. Otolaryngol. Head Neck Surg., vol.117, pp.1158-1161, 1991 https://doi.org/10.1001/archotol.1991.01870220106019
  3. H. Takahashi and I. Sando, 'Computer-aided 3-D temporal bone anatomy for cochlear implant surgery,' Laryngoscope, vol.100, pp.417-421, 1990
  4. R. Frankenthaler, V. Moharir, R. Kikinis, P. V. Kipshagen, F. Jolesz, C. Umans, and M. P. Fried, 'Virtual Otoscopy, Computers in Otolaryngology,' vol.31, pp.383-392, 1998
  5. S. K. Yoo, G. Wang, J. T. Rubinstein, M. W. Skinner and M. W. Vannier, 'Three-dimensional modelling and visualization of the cochlea on the internet,' IEEE Tran. Info. Tech. in Biomed., June, 2000 https://doi.org/10.1109/4233.845207
  6. T. Himi, A. Kataura, M. Sakata, Y. Odawara, J. Satoh, and M. Sawaishi, 'Three-dimensional imaging of the temporal bone using a helical CT scan and its application in patients with cochlear implantation,' ORL; Journal of Oto-Rhino-Laryngology & its related specialties, vol.58, pp.298-300
  7. C. Yuan, E. Lin, J. Millard, and J. Hwang, 'Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood MR images,' Magnetic Resonance Imaging, vol.17, no.2, pp.257-266, 1999 https://doi.org/10.1016/S0730-725X(98)00162-3
  8. A. J. Bell and T. J. Sejnowski, 'Edges are the 'independent components' of natural scenes,' In Advances in neural information processing systems 9, pp.831-837, Cambridge: MIT Press, 1997
  9. A. J. Bell and T. J. Sejnowski, 'The 'independent components' of natural scenes are edge filters,' Vision Research, vol.37, no.23, pp.3327-3338, 1997 https://doi.org/10.1016/S0042-6989(97)00121-1
  10. A. J. Bell and T. J. Sejonwski, 'An information-maximization approach to blind separation and blind deconvolution,' Neural Computation, vol.7, no.6, pp.1129-1159, 1995 https://doi.org/10.1162/neco.1995.7.6.1129
  11. Te-Won Lee, Independent Component Analysis: Theory and Applications, Kluwer Academic Publishers, 1998
  12. P. Comon, 'Independent component analysis - a new concept?' Signal Processing, vol.36, pp.287-314, 1994 https://doi.org/10.1016/0165-1684(94)90029-9
  13. J. P. Nadal and N. Parga, 'Non-linear neurons in the low noise limit: a factorial code maximizes information transfer,' Network, vol.5, pp.565-581, 1994 https://doi.org/10.1088/0954-898X/5/4/008
  14. D. J. Field, 'What is the goal of sensory coding?' Neural Computation, vol.6, pp.559-601, 1994 https://doi.org/10.1162/neco.1994.6.4.559
  15. Y. J. Zhang, 'A survey on evaluation methods for image segmentation,' Pattern Recognition, vol.29, no.8, pp.1335-1346, 1996 https://doi.org/10.1016/0031-3203(95)00169-7
  16. A. M. Nazif and M. D. Levine, 'Low level image segmentation : an expert system,' IEEE Trans. PAMI-6, pp.555-577, 1984
  17. J. S. Weszka and A. Rosenfeld, 'Threshold evaluation techniques,' IEEE Trans. SMC-8, pp.622-629, 1978
  18. S. U. Lee, S. Y. Chung and R. H. Park, 'A comparative performance study of several global thresholding techniques for segmentation,' CVGIP, vol.52, pp.171-190, 1990 https://doi.org/10.1016/0734-189X(90)90053-X
  19. J. Liu and Y. H. Yang, 'Multiresolution Color Image Segmentation,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.16, no.7, 1994 https://doi.org/10.1109/34.297949