Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval

Jiayin Zhou, Yanling Chi, Weimin Huang, Wei Xiong, Wenyu Chen, Jimin Liu, Sudhakar K Venkatesh

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents the semiautomated segmentation of liver tumor from computed tomography (CT) scans under a hybrid support vector machine (SVM) framework and a content-based image retrieval prototype system based on multiphase CT images to support the decision-making for liver tumor characterization. It presents a three-stage, hybrid support vector machine (HSVM)-based approach for liver tumor segmentation. In this method, HSVM is a seamless and natural connection of one-class support vector machine (OSVM) and binary support vector machine (BSVM) by a boosting tool. The chapter introduces a content-based image retrieval (CBIR) prototype system based on multiphase CT images to help radiologists in characterizing focal liver tumors. With further development and validation, these methods have the potential of being adopted as image analysis tools to assist liver tumor volumetry and characterization for cancer diagnosis, treatment planning, and assessment of therapy response.

Original languageEnglish (US)
Title of host publicationBiomedical Image Understanding: Methods and Applications
Publisherwiley
Pages325-360
Number of pages36
ISBN (Electronic)9781118715321
ISBN (Print)9781118715154
DOIs
StatePublished - Feb 13 2015

Keywords

  • Binary support vector machine (BSVM)
  • Class support vector machine (OSVM)
  • Computed tomography (CT)
  • Content-based image retrieval (CBIR)
  • Hybrid support vector machine (HSVM)
  • Liver tumor segmentation

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Zhou, J., Chi, Y., Huang, W., Xiong, W., Chen, W., Liu, J., & Venkatesh, S. K. (2015). Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval. In Biomedical Image Understanding: Methods and Applications (pp. 325-360). wiley. https://doi.org/10.1002/9781118715321.ch9