Liver Lesion Detection Based on Two-Stage Saliency Model with Modified Sparse Autoencoder

  • Yixuan YuanEmail author
  • Max Q.-H. Meng
  • Wenjian Qin
  • Lei Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Liver lesion detection is an important task for diagnosis and surgical planning of focal liver disease. The large numbers of images in routine liver CT studies, in addition to their high diversity in appearance, have been hurdles for detecting all lesions by visual inspection. Automated methods for lesion identification are desirable, but the results of current approaches are limited due to the diversity of the training sets and the extensive tuning of parameters. In this paper, we propose a novel saliency model for lesion detection in CT images. First, we segment the image into multi-scale patch sizes. Then, a two-stage saliency model is proposed to detect liver lesions. In the first stage, we calculate the gray level contrast saliency map based on a prior knowledge to reduce the influence of blood vessels in CT images. In the second stage, we propose a modified sparse autoencoder (SAE) with neighbourhood information to learn discriminative features directly from raw patch features and adopt Locality-constrained Linear Coding (LLC) method to encode the obtained discriminative features of each patch. Then the second saliency map is calculated based on feature uniqueness and spatial distribution of patches. Followed by an appropriate mapping fusion, the liver lesions can be detected well. With \(7\times 7\) sized patches, a 120 visual word dictionary, and 14 feature dimension, our model achieved 90.81% accuracy for lesion detection.



We appreciate valuable suggestions by Prof. Daniel Rubin and Dr. Assaf Hoogi in Stanford University.


  1. 1.
    Harel, J., Koch, C., Perona, P., et al.: Graph-based visual saliency. In: NIPS, vol. 1, p. 5 (2006)Google Scholar
  2. 2.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  3. 3.
    Jiang, L., Xu, M., Ye, Z., Wang, Z.: Image saliency detection with sparse representation of learnt texture atoms. In: CVPR Workshops, pp. 54–62 (2015)Google Scholar
  4. 4.
    Knops, Z.F., Maintz, J.A., Viergever, M.A., Pluim, J.P.: Normalized mutual information based registration using k-means clustering and shading correction. Med. Image Anal. 10(3), 432–439 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Li, C., Wang, X., Eberl, S., Fulham, M., Yin, Y., Chen, J., Feng, D.D.: A likelihood and local constraint level set model for liver tumor segmentation from CT volumes. IEEE Trans. Biomed. Eng. 60(10), 2967–2977 (2013)CrossRefGoogle Scholar
  6. 6.
    Park, S.-J., Seo, K.-S., Park, J.-A.: Automatic hepatic tumor segmentation using statistical optimal threshold. In: Sunderam, V.S., Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 934–940. Springer, Heidelberg (2005). doi: 10.1007/11428831_116 CrossRefGoogle Scholar
  7. 7.
    Safdari, M., Pasari, R., Rubin, D., Greenspan, H.: Image patch-based method for automated classification and detection of focal liver lesions on CT. In: SPIE, p. 86700Y (2013)Google Scholar
  8. 8.
    Wang, J., Borji, A., Kuo, C.C.J., Itti, L.: Learning a combined model of visual saliency for fixation prediction. IEEE Trans. Image Process. 25(4), 1566–1579 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR, pp. 3360–3367 (2010)Google Scholar
  10. 10.
    Yang, W., Feng, Q., Huang, M., Lu, Z., Chen, W.: A non-parametric method based on NBNN for automatic detection of liver lesion in CT images. In: ISBI, pp. 366–369 (2013)Google Scholar
  11. 11.
    Yuan, Y., Meng, M.Q.H.: Deep learning for polyp recognition in wireless capsule endoscopy images. Med. Phys. 44(4), 1379–1389 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Zheng, Y., Liu, Y., Yang, J., Zhao, Y., Chen, D., Wang, Y.: Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy. IEEE Trans. Med. Imaging 36(1), 51–63 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yixuan Yuan
    • 1
    • 3
    Email author
  • Max Q.-H. Meng
    • 3
  • Wenjian Qin
    • 2
  • Lei Xing
    • 1
  1. 1.Department of Radiation OncologyStanford UniversityStanfordUSA
  2. 2.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  3. 3.Department of Electronic EngineeringChinese University of Hong KongHong KongThe People’s Republic of China

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