Statistical analysis of multivariate planar curves and applications to X-ray classification
This work provides a novel method for medical image classification, specifically for X-ray analysis, though it appears incremental as it builds on existing functional classification techniques.
The authors tackled the problem of using segmented image contours as predictors for supervised classification in medical imaging, developing a new approach for multivariate planar curves that addresses alignment issues and achieves robust detection of cardiomegaly in X-rays.
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.