A Proposal for Color Segmentation in PET/CT-Guided Liver Images

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Automatic methods for detection and segmentation of tumor have become essential for the computer-oriented diagnosis of liver tumors in images. Tumor Segmentation in grayscale medical images can be difficult since the intensity values between tumor and healthy tissue is very close. Positron emission tomography combined with computed tomography (PET/CT) provides more accurate measurements of tumor size than is possible with visual assessment alone. In this paper, a new method for the detection of liver tumor in PET/CT scans is proposed. The images are denoised using median filter and binary tree quantization clustering algorithm is used for segmentation. Finally image dilation and erosion, boundary detection, ROI selection and shape feature extraction are applied on the selected cluster to identify the shape of the tumor.


Denoising Liver segmentation Tumor Detection Binary Tree Quantization Algorithm Shape Feature Extraction 


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  1. 1.
    Masuda, Y., Tateyama, T., Xiong, W., Zhou, J., Wakamiya, M., Kanasaki, S., Furukawa, A., Chen, Y.W.: Liver Tumor Detection in CT images by Adaptive Contrast Enhancement and the EM/MPM Algorithm. In: 18th IEEE Conference on Image Processing, pp. 1421–1424 (September 2011)Google Scholar
  2. 2.
    Hounsfield, G.N.: Computerized Transverse Axial scanning Tomography: Part 1, Description of the System. British Journal of Radiology 46, 1016–1022 (1973)CrossRefGoogle Scholar
  3. 3.
    Lipinski, B., Herzog, H., Kops, E.R., Oberschelp, W., Muleer-Gartner, H.W.: Expectation Maximization Reconstruction of Positron Emission Tomography Images using Anatomical magnetic Resonance Information. IEEE Transaction on Medical Imaging 16, 129–136 (1997)CrossRefGoogle Scholar
  4. 4.
    Bazille, A., Guttman, M.A., Mcveigh, E.R., Zerhouni, E.A.: Impact of Semiautomated versus Manual Image Segmentation Errors on Myocardial Strain Calculation by Magnetic Resonance Tagging. Investigative Radiology 29, 427–433 (1994)CrossRefGoogle Scholar
  5. 5.
    Anger, H.: Use of Gamma-Ray Pinhole Camera for viva studies. In: A Nature Conference on Nuclear Reprogramming and the Cancer Genome, vol. 170, pp. 200–204 (1952)Google Scholar
  6. 6.
    Ouyang, X., Wang, W.H., Johnson, V.E., Hu, X., Chen, C.T.: Incorporation of Correlated Structural Images in PET Image Reconstruction. IEEE Transactions on Medical Imaging 13, 627–640 (2002)CrossRefGoogle Scholar
  7. 7.
    Akgul, Y.S., Kambhamettu, C., Stone, M.: Extraction and Tracking of the Tongue Surface from Ultrasound Image Sequences. In: 1998 IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp. 298–303 (June 1998)Google Scholar
  8. 8.
    Abeyratne, U.R., Petropulu, A.P., Reid, J.M.: On modeling the Tissue Response from Ultrasonic B-scan. IEEE Transactions on Medical Imaging 2, 479–490 (1996)CrossRefGoogle Scholar
  9. 9.
    Foruzan, A.H., Zoroofi, R.A., Hori, M., Sato, Y.: A Knowledge-based Technique for Liver Segmentation in CT Data. Computerized Medical Imaging and Graphics 33, 567–587 (2009)CrossRefGoogle Scholar
  10. 10.
    Zhang, X., Tian, J., Deng, K., Yongfang, W., Xiuli, I.: Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection. IEEE Transactions on Biomedical Engineering 57, 2622–2626 (2010)CrossRefGoogle Scholar
  11. 11.
    Rusko, L., Bekes, G., Fidrich, M.A.: Automatic Segmentation of the Liver from Multi- and Single-Phase Contrast-Enhanced CT. Medical Image Analysis 13, 871–882 (2009)CrossRefGoogle Scholar
  12. 12.
    Masoumi, H., Behrad, A., Pourmina, M.A., Roosta, A.: Automatic liver segmentation in MRI Images using an Iterative Watershed Algorithm and Artificial Neural Network. Biomedical Signal Processing and Control 7, 429–437 (2012)CrossRefGoogle Scholar
  13. 13.
    Lezoray, O., Charrier, C.: Color Image Segmentation using Morphological Clustering and Fusion with Automatic Scale Selection. Pattern Recognition Letters 30, 397–406 (2009)CrossRefGoogle Scholar
  14. 14.
    Escobar, M.M., Foo, J.L., Winer, E.: Colorization of CT images to Improve Tissue Contrast for Tumor Segmentation. Computers in Biology and Medicine 42, 1170–1178 (2012)CrossRefGoogle Scholar
  15. 15.
    Necib, H., Garcia, C., Wagner, A., Vanderleinden, B., Emonts, P., Hendlisz, A., Flamen, P., Buvat, I.: Detection and Characterization of Tumor Changes in 18FFDG Patient Monitoring using Parametric Imaging. J. of Nucl. Med. 52, 354–361 (2011)CrossRefGoogle Scholar
  16. 16.
    Lartizien, C., Francisco, S.M., Prost, R.: Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study. IEEE Transactions on Nuclear Science 59, 102–112 (2012)CrossRefGoogle Scholar
  17. 17.
    Changyang, L., Wanga, X., Xiaa, Y., Eberlb, S., Yinc, Y., Feng, D.D.: Automated PET-guided Liver Segmentation from Low-Contrast CT Volumes using Probabilistic Atlas. Computer Methods and Programs in Biomedicine 107, 164–174 (2011)Google Scholar
  18. 18.
    Blechacz, B., Gores, G.J.: PET scan for Hepatic Mass. Hepatology 52, 2186–2191 (2010)CrossRefGoogle Scholar
  19. 19.
    Ming, X., Feng, Y., Guo, Y., Yang, C.: A New Automatic Segmentation Method for Lung Tumor Based on SUV threshold on 18F-FDG PET images. In: 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), pp. 5–8 (July 2012)Google Scholar
  20. 20.
    Yong, X., Stefan, E., Lingfeng, W., Michael, F., David, D.D.: Dual-Modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information. Computerized Medical Imaging and Graphics 36, 47–53 (2011)Google Scholar
  21. 21.
    Belhassen, S., Zaidi, H.: A Novel Fuzzy C-means Algorithm for Unsupervised Heterogeneous Tumor Quantification in PET. Medical Physics 37, 1309–1324 (2010)CrossRefGoogle Scholar
  22. 22.
    Geets, X., Lee, J.A., Bol, A., Lonneux, M., Gregoire, V.: A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur. J. of Nucl. Med. Mol. Imaging 34, 1427–1438 (2007)CrossRefGoogle Scholar
  23. 23.
    Hatt, M., Rest, C.L., Turzo, A., Roux, C., Visvikis, D.: Fuzzy Logically Adaptive Bayesian Segmentation Approach for Volume Determination in PET. IEEE Transactions on Medical Imaging 28, 881–893 (2009)CrossRefGoogle Scholar
  24. 24.
    Li, H., Thorstad, W.L., Biehl, K.J., Laforest, R., Su, Y., Shoghi, K.I., Donnelly, E.D., Low, D.A., Lu, W.: A Novel PET Tumor Delineation Method based on Adaptive region-Growing and Dual-Front Active Contours. Medical Physics 35, 3711–3721 (2008)CrossRefGoogle Scholar
  25. 25.
    Baardwijk, A., Bosmans, G., Boersma, L.: PET-CT based Auto-contouring in Non- Small-Cell Lung Cancer correlates with Pathology and reduces Interobserver Variability in the Delineation of the Primary Tumor and involved Nodal Volumes. International Journal of Radiation and Oncology, Biology and Physics 68, 771–778 (2007)CrossRefGoogle Scholar
  26. 26.
    Potesil, V., Huang, X., Zhou, X.: Automated Tumor Delineation using Joint PET/CT information. In: Proc. SPIE International Symposium on Medical Imaging: Computer-Aided Diagnosis, vol. 65142 (March 2007)Google Scholar
  27. 27.
    Xia, Y., Wen, L., Eberl, S., Fulham, M., Fend, D.: Segmentation of Dual Modality Brain PET/CT images using MAP-MRF model. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 107–110 (October 2008)Google Scholar
  28. 28.
    Yu, H., Caldwell, C., Mah, K.: Automated Radiation targeting in head-and-neck cancer using Region-based Texture Analysis of PET and CT images. International Journal of Radiation and Oncology, Biology and Physics 75, 618–625 (2009)CrossRefGoogle Scholar
  29. 29.
    Yu, H., Caldwell, C., Mah, K., Mozeg, D.: Coregistered FDG PET/CT-based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning. IEEE Transactions on Medical Imaging 28, 374–383 (2009)CrossRefGoogle Scholar
  30. 30.
    Gunjal, B.L., Mali, S.N.: ROI Based Embedded Watermarking of Medical Images for Secured Communication in Telemedicine. International J. Comp. and Commun. Eng. 68, 815–820 (2012)Google Scholar
  31. 31.
    Centre for Control and Information Services, National Centre, Japan,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

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