Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images
Article first published online: 4 MAR 2014
© 2014 The Authors Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 33, Issue 6, pages 190–204, September 2014
How to Cite
Zukić, D., Vlasák, A., Egger, J., Hořínek, D., Nimsky, C. and Kolb, A. (2014), Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images. Computer Graphics Forum, 33: 190–204. doi: 10.1111/cgf.12343
- Issue published online: 25 SEP 2014
- Article first published online: 4 MAR 2014
- I.4.6 [Image processing and computer vision]: Segmentation Pixel classification
The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X-ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola–Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely—0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction.