A weighting strategy for Active Shape Models
This incremental improvement addresses segmentation accuracy for medical imaging applications, specifically in fluoroscopic X-ray analysis.
The paper tackled the problem of unreliable landmark detection in Active Shape Models for segmentation by proposing a weighting strategy based on covariance of residuals and detector range checks, and demonstrated that it outperforms standard ASM and other weighted methods on femur segmentation in fluoroscopic X-ray images.
Active Shape Models (ASM) are an iterative segmentation technique to find a landmark-based contour of an object. In each iteration, a least-squares fit of a plausible shape to some detected target landmarks is determined. Finding these targets is a critical step: some landmarks are more reliably detected than others, and some landmarks may not be within the field of view of their detectors. To add robustness while preserving simplicity at the same time, a generalized least-squares approach can be used, where a weighting matrix incorporates reliability information about the landmarks. We propose a strategy to choose this matrix, based on the covariance of empirically determined residuals of the fit. We perform a further step to determine whether the target landmarks are within the range of their detectors. We evaluate our strategy on fluoroscopic X-ray images to segment the femur. We show that our technique outperforms the standard ASM as well as other more heuristic weighted least-squares strategies.