Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field

• Published in: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Vol. 2010; pp. 3117 - 3120
• Main Authors: Jaehan Koh, Taehyong Kim, Chaudhary, V, Dhillon, G
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2010
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Artificial Intelligence; Biomedical imaging; Computed tomography; Dura Mater - anatomy & histology; Female; Humans; Image edge detection; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Irrigation; Lumbar Vertebrae - pathology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Spinal cord; Spinal Cord - anatomy & histology
• ISBN: 1424441234; 9781424441235
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2010.5626097

A Computer-aided diagnosis (CAD) system aims to facilitate characterization and quantification of abnormalities as well as minimize interpretation errors caused by tedious tasks of image screening and radiologic diagnosis. The system usually consists of segmentation, feature extraction and diagnosis, and segmentation significantly affects the diagnostic performance. In this paper, we propose an automatic segmentation method that extracts the spinal cord and the dural sac from T2-weighted sagittal magnetic resonance (MR) images of lumbar spine without the need of any human intervention. Our method utilizes a gradient vector flow (GVF) field to find the candidate blobs and performs a connected component analysis for the final segmentation. MR Images from fifty two subjects were employed for our experiments and the segmentation results were quantitatively compared against reference segmentation by two medical specialists in terms of a mutual overlap metric. The experimental results showed that, on average, our method achieved a similarity index of 0.7 with a standard deviation of 0.0571 that indicated a substantial agreement. We plan to apply this segmentation method to computer-aided diagnosis of many lumbar-related pathologies.