Multi-stage Thresholded Region Classification for Whole-Body PET-CT Lymphoma Studies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Positron emission tomography computed tomography (PET-CT) is the preferred imaging modality for the evaluation of the lymphomas. Disease involvement in the lymphomas usually appear as foci of increased Fluorodeoxyglucose (FDG) uptake. Thresholding methods are applied to separate different regions of involvement. However, the main limitation of thresholding is that it also includes regions where there is normal FDG excretion and FDG uptake (NEUR) in structures such as the brain, bladder, heart and kidneys. We refer to these regions as NEURs (the normal excretion and uptake (of FDG) regions). NEURs can make image interpretation problematic. The ability to identify and label NEURs and separate them from abnormal regions is an important process that could improve the sensitivity of lesion detection and image interpretation. In this study, we propose a new method to automatically separate NEURs in thresholded PET images. We propose to group thresholded regions of the same structure with spatial and texture based clustering; we then classified NEURs on PET-CT contextual features. Our findings were that our approach had better accuracy when compared to conventional methods.


Standard Uptake Value Contextual Feature Thresholding Method Positron Emission Tomography Compute Tomography Image Positron Emission Tomography Compute Tomography 
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  1. 1.
    Bi, L., Kim, J., Wen, L., Feng, D.D.: Automated and robust percist-based thresholding framework for whole body pet-ct studies. In: EMBC 2012, pp. 5335–5338. IEEE (2012)Google Scholar
  2. 2.
    Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM TIST 2(3), 27 (2011)Google Scholar
  3. 3.
    Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)Google Scholar
  4. 4.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  5. 5.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE. T Pattern. Anal. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  6. 6.
    Freudenberg, L., Antoch, G., Schütt, P., Beyer, T., Jentzen, W., Müller, S.P., Görges, R., Nowrousian, M.R., Bockisch, A., Debatin, J.F.: Fdg-pet/ct in re-staging of patients with lymphoma. Eur. J. Nucl. Med. Mol. I. 31(3), 325–329 (2004)CrossRefGoogle Scholar
  7. 7.
    Hu, S., Hoffman, E.A., Reinhardt, J.M.: Automatic lung segmentation for accurate quantitation of volumetric x-ray ct images. IEEE. T. Med. Imaging. 20(6), 490–498 (2001)CrossRefGoogle Scholar
  8. 8.
    Kakar, M., Olsen, D.R.: Automatic segmentation and recognition of lungs and lesion from ct scans of thorax. Comput. Med. Imag. Grap. 33(1), 72–82 (2009)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Hu, Y., Eberl, S., Feng, D., Fulham, M.: A fully automatic bed/linen segmentation for fused pet/ct mip rendering. In: Society of Nuclear Medicine Annual Meeting Abstracts, vol. 49, p. 387. Soc. Nuclear Med (2008)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Pescia, D., Paragios, N., Chemouny, S.: Automatic detection of liver tumors. In: ISBI 2008, pp. 672–675. IEEE (2008)Google Scholar
  12. 12.
    Song, Y., Cai, W., Kim, J., Feng, D.D.: A multistage discriminative model for tumor and lymph node detection in thoracic images. IEEE. T. Med. Imaging. 31(5), 1061–1075 (2012)CrossRefGoogle Scholar
  13. 13.
    Wahl, R.L., Jacene, H., Kasamon, Y., Lodge, M.A.: From recist to percist: evolving considerations for pet response criteria in solid tumors. J. Nucl. Med. 50(Suppl. 1), 122S–150S (2009)Google Scholar
  14. 14.
    Wu, B., Khong, P.-L., Chan, T.: Automatic detection and classification of nasopharyngeal carcinoma on pet/ct with support vector machine. IJCARS 7(4), 635–646 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Information TechnologiesUniversity of SydneyAustralia
  2. 2.Med-X Research InstituteShanghai Jiao Tong UniversityChina
  3. 3.Department of Molecular ImagingRoyal Prince Alfred HospitalAustralia
  4. 4.Sydney Medical SchoolUniversity of SydneyAustralia

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