Computer aided detection of spina bifida using nearest neighbor classification with curvature scale space features of fetal skulls extracted from ultrasound images

摘要

This paper addresses the problem of detecting the common neural tube defect of spina bifida by a computer aided detection (CAD) system. We propose a Method which extracts the curvature scale space (CSS) features of fetal skull contours viewed in the ultrasound (US) modality and performs nearest neighbor (kNN) classification on those features having the desired properties of invariance with respect to translation, orientation and scale changes, thus improving robustness. The distance between two sets of CSS features, each set corresponding to the description of the contour of a particular skull, is measured as the cost of matching the two sets of CSS features. Such a CAD system may act as a second observer and help experts in prenatal diagnosis.