title: Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images.
abstract: Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression.
authors.affiliation: Polytechnique Montréal, 2900 boul. Édouard-Montpetit, Montréal, Canada QC H3T 1J4. Electronic address: florian.berton@polymtl.ca.
authors.forename: Florian
authors.initials: F
authors.lastname: Berton
authors.affiliation: Polytechnique Montréal, 2900 boul. Édouard-Montpetit, Montréal, Canada QC H3T 1J4; Sainte-Justine University Hospital Research Centre, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, Canada QC H3T 1C4. Electronic address: farida.cheriet@polymtl.ca.
authors.forename: Farida
authors.initials: F
authors.lastname: Cheriet
authors.affiliation: Sainte-Justine University Hospital Research Centre, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, Canada QC H3T 1C4. Electronic address: marie-claude_miron@ssss.gouv.qc.ca.
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Miron
authors.affiliation: École de technologie supérieure, 1100 Rue Notre-Dame O., Montréal, Canada QC H3C 1K3; Sainte-Justine University Hospital Research Centre, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, Canada QC H3T 1C4. Electronic address: catherine.laporte@etsmtl.ca.
authors.forename: Catherine
authors.initials: C
authors.lastname: Laporte
mesh_terms: D000162:Acoustics; D000465:Algorithms; D011336:Probability; D013131:Spine / Q000000981:diagnostic imaging*; D014463:Ultrasonography
keywords: Acoustic shadow; Image processing; Image segmentation; Pattern classification; Scoliosis; Ultrasound; Vertebrae