{"ID":2882056,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11780","arxiv_id":"2508.11780","title":"Statistical analysis of multivariate planar curves and applications to X-ray classification","abstract":"Recent developments in computer vision have enabled the availability of segmented images across various domains, such as medicine, where segmented radiography images play an important role in diagnosis-making. As prediction problems are common in medical image analysis, this work explores the use of segmented images (through the associated contours they highlight) as predictors in a supervised classification context. Consequently, we develop a new approach for image analysis that takes into account the shape of objects within images. For this aim, we introduce a new formalism that extends the study of single random planar curves to the joint analysis of multiple planar curves-referred to here as multivariate planar curves. In this framework, we propose a solution to the alignment issue in statistical shape analysis. The obtained multivariate shape variables are then used in functional classification methods through tangent projections. Detection of cardiomegaly in segmented X-rays and numerical experiments on synthetic data demonstrate the appeal and robustness of the proposed method.","short_abstract":"Recent developments in computer vision have enabled the availability of segmented images across various domains, such as medicine, where segmented radiography images play an important role in diagnosis-making. As prediction problems are common in medical image analysis, this work explores the use of segmented images (t...","url_abs":"https://arxiv.org/abs/2508.11780","url_pdf":"https://arxiv.org/pdf/2508.11780v2","authors":"[\"Issam-Ali Moindjié\",\"Marie-Hélène Descary\",\"Cédric Beaulac\"]","published":"2025-08-15T19:13:27Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.CV\",\"stat.ML\"]","methods":"[]","has_code":false}