WL Operator: A Robust Quasi High-Pass Filter for Edge Detection in Medical Images

2023-07-08T00:57:21+02:00

In this study, we propose a robust edge detection method for medical images using the WL operator (Wang and Lin). The operator is based on a kernel-based algorithm, similar to conventional edge detectors, but with an adaptive and mathematically formulated approach. By expressing the detector as a quadratic form of the Toeplitz matrix, we exploit its highly structured internal architecture and spatial isotropic symmetry. The WL operator addresses common edge detection challenges such as fragmentation, position dislocation, and thinness loss, while being robust to noise and efficient in extracting crucial edge features. Comparative evaluations against other edge detectors, using Pratt's figure of merits and expert visual analog scale scores, demonstrate the superior performance of the WL operator. Furthermore, the operator shows promising results in various medical imaging modalities, including X-ray, CT, and MRI, encouraging further investigation.

Impact of Model Parameterization and Basis Function Order on Predictive Accuracy in Model-Based Glycaemic Control

2023-07-08T01:03:27+02:00

Glycaemic control (GC) is crucial in critical care settings to reduce mortality and improve clinical outcomes. Model-based GC algorithms offer personalized and effective predictive control. This study investigates the influence of model parameterization and basis function order on the accuracy of future predictions of insulin sensitivity (SI) variability, a key determinant of glucose dynamics. Glycaemic data from 30 critically ill patient episodes were utilized to fit a glucose dynamics model, with SI identified using b-spline basis functions. Stochastic maps of SI changes over time were created based on the identified SI profiles, enabling the estimation of SI prediction distributions for future time steps. The impact of varying the number of basis functions (M) relative to data points (N) and the basis function order (d) on predictive accuracy was evaluated. Results showed that increasing the model parameterization led to wider prediction distributions, while higher basis function order resulted in tighter prediction distributions and more accurate predictions. The Akaike Information Criterion analysis suggested an optimal M:N ratio of approximately 0.45 for all basis function orders. These findings highlight the potential of improved model parameterization to enhance predictive capability in GC algorithms, ultimately benefiting patient outcomes in critical care settings.

Journal of Data-Driven Engineering Systems

WL Operator: A Robust Quasi High-Pass Filter for Edge Detection in Medical Images

Impact of Model Parameterization and Basis Function Order on Predictive Accuracy in Model-Based Glycaemic Control