Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery

Zhenyu Tang, Sahar Ahmad, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with state-of-the-art methods by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.

Original languageEnglish
Article number8332964
Pages (from-to)2224-2235
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number10
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
Manuscript received February 1, 2018; revised March 14, 2018 and March 28, 2018; accepted April 2, 2018. Date of publication April 6, 2018; date of current version October 1, 2018. This work was supported in part by NIH under Grant AG053867, Grant EB006733, and Grant EB008374, in part by the National Natural Science Foundation of China under Grant 61502002, and in part by the Natural Science Foundation of Anhui Province Education Department under Grant KJ2015A008. (Corresponding author: Dinggang Shen.) Z. Tang is with the School of Computer Science and Technology, Anhui University, Anhui, Hefei 230601, China, and also with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail:

Publisher Copyright:
© 2017 IEEE.


  • Low-rank
  • image recovery
  • multi-atlas segmentation
  • spatial constraint
  • tumor brain image

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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