Interactive Mesh Smoothing for Medical Applications

Abstract

Surface models derived from medical image data often exhibit artefacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation of uniform and anisotropic filters, model quality is evaluated in real-time and provided to the user to support the mental optimization of input parameters. This is achieved by means of quality graphs and quality bars. Moreover, this framework is used to find appropriate smoothing parameters automatically and to provide data-specific parameter suggestions. These suggestions are employed to generate a preview gallery with different smoothing suggestions. The preview functionality is additionally used for the inspection of specific artefacts and their possible reduction with different parameter sets.Additional Supporting Information may be found in the online version of this article at the publisher's web site:Video S1: Real-time mesh smoothing.Video S2: Quality graphs and quality bars.Video S3: Parameter suggestions.

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

“…Moench et al [MAP10] present a modification to common mesh smoothing algorithms to preserve non‐artifact features by focusing on previously identified staircase artifacts. To further improve the handling of mesh smoothing filters, Moench et al [MLK*13] introduce model quality graphs and model quality bars which are evaluated in real‐time and presented to the user to perform parameter adjustments and to provide immediate visual feedback on accuracy and smoothness.…”

Visualization and Extraction of Carvings for Heritage Conservation

“…After a preview image has been selected, the corresponding parameter setting can be modified to fine-tune the result. Our user-interface for this step is inspired by the work of Mönch et al [26]. When pressing the right mouse button, the user can alter the two parameters by mouse movements.…”

Effective mesh smoothing for haptic rendering in medical applications

“…As a result, the elimination of staircases can be achieved, but as a side-effect the non-staircases regions would get altered, such as shape distortion, volume shrinkage, feature blurring, etc. We adopte the method of Moench et al [18], which presented a staircase-sensitive Laplace filter to assign an adaptive resampling factor for each mesh vertex according to the distance to its closest staircase vertex. The vertex resampling is formulated as…”

“…Thus, if these special facets are clustered into one cluster, the image slices direction can be estimated. Moench et al [18] proposed an approach to approximate the image slices direction Specifically, the normals of all facets are first projected into a unit sphere (each normal is called a sample later). Secondly, the density of each sample is computed by counting the number of samples within a given radius (default value is 0.1), and samples with density lower than a pre-specified threshold are removed.…”

“…Recently, some researchers attempted to solve the artifacts on medical surface by carefully selecting existing filters (e.g., in [17] [18]) or by adapting some of them (e.g., in [19] [3]). However, these efforts are still based on the traditional smoothing framework, which cannot completely avoid the aforementioned disadvantages.…”