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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)

Abstract

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.

Notes

Acknowledgments

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

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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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