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Learning Longitudinal Deformations for Adaptive Segmentation of Lung Fields from Serial Chest Radiographs

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Medical Imaging and Augmented Reality (MIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5128))

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Abstract

We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.

This work is supported by Science and Technology Commission of Shanghai Municipality of China (Grant number: 06dz22103).

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References

  1. van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.A.: Computer-Aided Diagnosis in Chest Radiography: a Survey. IEEE Trans. On Medical Imaging 20(12), 1228–1241 (2001)

    Article  Google Scholar 

  2. van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of Anatomical Structures in Chest Radiographs using supervised methods: a Comparative Study on a Public Database. Medical Image Analysis 10, 19–40 (2006)

    Article  Google Scholar 

  3. Cootes, T.F., Taylor, C.J.: Statistical Models of appearance for Computer Vision. Technical Report, Wolfson Image Analysis Unit, University of Manchester (2001)

    Google Scholar 

  4. Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.: Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-based and Patient-specific Shape Statistics. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 83–91. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Davatzikos, C., Tao, X., Shen, D.: Hierarchical Active Shape Models Using the Wavelet Transform. IEEE Trans on Medical Imaging 22(3), 414–423 (2003)

    Article  Google Scholar 

  6. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  7. Hontani, H., Deguchi, K.: Primitive Curve Generation Based on Multiscale Contour Figure Approximation. In: Proceeding of 15th International Conference on Pattern Recognition, vol. 2, pp. 887–890 (2000)

    Google Scholar 

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Takeyoshi Dohi Ichiro Sakuma Hongen Liao

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© 2008 Springer-Verlag Berlin Heidelberg

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Shi, Y., Shen, D. (2008). Learning Longitudinal Deformations for Adaptive Segmentation of Lung Fields from Serial Chest Radiographs. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_45

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  • DOI: https://doi.org/10.1007/978-3-540-79982-5_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79981-8

  • Online ISBN: 978-3-540-79982-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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