Automated Delineation of Costophrenic Recesses on Chest Radiographs

  • Prashant A. AthavaleEmail author
  • P. S. Puttaswamy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)


The lung image segmentation using a model-based approach is a challenge owing to the sheer complexity and variability of the lung shape in a given data set. As a part of our effort to segment the lungs, we report a method to delineate the costophrenic (CP) recess without the human intervention. Active shape model (ASM) is used to point to the probable area of the CP recess, and a prior knowledge-based processing delineates the CP recess and hence determines the angle. The proposed method is fast and shows satisfactory results. It is intended to be used as a preprocessing step in segmenting the lungs’ contour. The proposed method also can be used to initialize the model contour in any other ASM-based lung segmentation algorithms. The algorithm was tested on 45 non-nodule lung images from the JSRT database. An average accuracy of 87.02% is achieved. A comparison of the results of proposed method and gold standard which is obtained by manual delineation is given.


Costophrenic angle Active shape model Lung segmentation Computer-aided diagnosis Sensitivity Specificity Jaccard index 


  1. 1.
    Cootes, T., Taylor, C., Cooper, D., & Graham, J. (1995). Active shape models—their training and application. Computer Vision and Image Understanding, 61, 38–59.Google Scholar
  2. 2.
    Tsai, A., Yezzi, A., Wells III, W. M., Tempany, C. M., Tucker, D., Fan, A., et al. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2), 137–54. PMID: 12715991.CrossRefGoogle Scholar
  3. 3.
    Paragios, N. & Deriche, R. (1998). Geodesic active regions for texture segmentation. INRIA, Sophia Antipolis, France, Res. Rep. 3440.Google Scholar
  4. 4.
    Shi, Y., Q, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., & Shen, D. (2008). Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Transactions on Medical Imaging, 27(4), 481–494./ Scholar
  5. 5.
    Maduskar, P., Philipsen, R. H., Melendez, J., Scholten, E., Chanda, D., Ayles, H., et al. (2016). Automatic detection of pleural effusion in chest radiographs. Medical Image Analysis, 28, 22–32. Epub 2015 December 1.CrossRefGoogle Scholar
  6. 6.
    Wan Ahmad, W. S. H. M., & Ahmad Fauzi, M. F., & Zaki, W. (2015). Abnormality detection for infection and fluid cases in chest radiograph (pp. 62–67).
  7. 7.
    Campadelli, P., & Casiraghi, E. (2005). Lung field segmentation in digital postero-anterior chest radiographs. In S. Singh, M. Singh, C. Apte, & P. Perner (Eds.), Pattern recognition and image analysis (vol. 3687, pp. 736–745). Lecture Notes in Computer Science. Springer, Heidelberg, Germany.Google Scholar
  8. 8.
    Abi-Nahed, J., Jolly, M. P., & Yang, G. Z. (2006). Robust active shape models: a robust, generic and simple automatic segmentation tool. In R. Larsen, M. Nielsen, J. Sporring (Eds.), Medical Image Computing and Computer-Assisted Intervention—MICCAI.Google Scholar
  9. 9.
    De la Torre, F., & Black, M. J. (2003). A framework for robust subspace learning. International Journal of Computer Vision, 54(1–3), 117–142.Google Scholar
  10. 10.
    Behiels, G., Maes, F., Vandermeulen, D., & Suetens, P. (2002). Evaluation of image features and search strategies for segmentation of bone structures in radiographs using active shape models. Medical Image Analysis, 6(1), 47–62.CrossRefGoogle Scholar
  11. 11.
    Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi T., Komatsu, K., et al. (2000). Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR 174, 71–74.Google Scholar
  12. 12.
    van Ginneken, B., Stegmann, M. B. & Loog, M. (2006). Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database. Medical Image Analysis, 10(1), 19–40.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of E&EEBMS Institute of Technology & ManagementBengaluruIndia
  2. 2.Department of E&EEPES College of EngineeringMandyaIndia

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