We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user's role while achieving good segmentation accuracy.
Bibliographical noteFunding Information:
Funding: This research was supported in parts by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2B4015016) in support of Dosik Hwang, and National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health in support of Won C. Bae (Grant Number R01 AR066622). The contents of this paper are the sole responsibility of the authors and do not necessarily represent the official views of the sponsoring institutions.
© 2018 by the authors.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes