Lumbar spine posterior corner detection in X-rays using Haar-based features

Shahin Ebrahimi , Elsa Angelini (1) , Laurent Gajny (2) , Wafa Skalli (2, 3, 4)

1 Télécom ParisTech
2 Institut de Biomecanique Humaine Georges Charpak
3 LBM - Laboratoire de biomécanique
4 I2M - Institut de Mécanique et d'Ingénierie de Bordeaux

3D reconstruction of the spine using biplanar X-rays remains approximate and requires human-machine interactions to adjust the position of important features such as vertebral corners and endplates. The purpose of this study is to develop a method to extract automatically the accurate position of lumbar vertebrae posterior corners. In the proposed method we select corner point candidates from an initial edge map. A dedicated pipeline is designed to discard unwanted candidates, involving polyline simplification, curvature thresholding and multiscale Haar filtering. Ultimately, we use a priori knowledge derived from an initial 3D spine model to define search areas and select the final corner points. The framework was tested on 21 biplanar X-rays from scoliotic children. Corner positions are compared with manual selections by two experts. The results report a localization accuracy between 0.7 and 1.6 mm, comparable to manual expert variability.

Mots clés: Biomedical Imaging; Image Analysis; Spine

Domaines: Sciences du Vivant [q-bio]; Informatique [cs]

Shahin Ebrahimi, Elsa Angelini, Laurent Gajny, Wafa Skalli. Lumbar spine posterior corner detection in X-rays using Haar-based features. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Apr 2016, Prague, Czech Republic. pp.1-4, ⟨10.1109/ISBI.2016.7493239⟩. ⟨hal-02181864⟩