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Normalized Euclidean Super-Pixels for Medical Image Segmentation

  • Feihong Liu
  • Jun Feng
  • Wenhuo Su
  • Zhaohui Lv
  • Fang Xiao
  • Shi Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10363)

Abstract

We propose a super-pixel segmentation algorithm based on normalized Euclidean distance for handling the uncertainty and complexity in medical image. Benefited from the statistic characteristics, compactness within super-pixels is described by normalized Euclidean distance. Our algorithm banishes the balance factor of the Simple Linear Iterative Clustering framework. In this way, our algorithm properly responses to the lesion tissues, such as tiny lung nodules, which have a little difference in luminance with their neighbors. The effectiveness of proposed algorithm is verified in The Cancer Imaging Archive (TCIA) database. Compared with Simple Linear Iterative Clustering (SLIC) and Linear Spectral Clustering (LSC), the experiment results show that, the proposed algorithm achieves competitive performance over super-pixel segmentation in the state of art.

Keywords

Medical image processing Segmentation Super-pixels Local compactness 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 61372046).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Feihong Liu
    • 1
  • Jun Feng
    • 1
  • Wenhuo Su
    • 2
  • Zhaohui Lv
    • 1
  • Fang Xiao
    • 1
  • Shi Qiu
    • 3
  1. 1.School of Information and TechnologyNorthwest UniversityXi’anChina
  2. 2.Center for Nonlinear Studies, Department of MathematicalsNorthwest UniversityXi’anChina
  3. 3.Xi’an Institute of Optics and Precision Mechanics of CASXi’anChina

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