Effective Computer-Assisted Automatic Cervical Vertebrae Extraction with Rehabilitative Ultrasound Imaging by using K-means Clustering - Hae-Jung Lee; Doo Heon Song; Kwang Baek Kim (Institute of Advanced Engineering and Science, 2016)
Neck pain is one of most common musculoskeletal condition resulting in significant clinical, social and economic costs. Muscles around cervical spine including deep neck flexors play a key role to support and control its stability, thus monitoring such muscles near cervical vertebrae is important. In this paper, we propose a fully automated computer assisted method to detect cervical vertebrae with K-means pixel clustering from ultrasonography. The method also applies a series of image processing algorithms to remove unnecessary organs and noises in the process. The experiment verifies that our approach is consistent with human medical experts’ decision to locate key measuring point for muscle analysis and successful in detecting cervical vertebrae accurately – successful in 48 out of 50 test cases (96%).