Display Settings:

Format

Send to:

Choose Destination
J Digit Imaging. 2013 Aug;26(4):786-96. doi: 10.1007/s10278-012-9568-1.

Semi-automatic segmentation of brain tumors using population and individual information.

Author information

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.

Abstract

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

PMID:
23319111
[PubMed - indexed for MEDLINE]
PMCID:
PMC3705006
[Available on 2014/8/1]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Springer
    Loading ...
    Write to the Help Desk