Towards improving edge quality using combinatorial optimization and a novel skeletonize algorithm

BMC Medical Imaging

Georges Hattab¹  Marvin Arnold²  Stefanie Speidel² 

[1] Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, 35032, Marburg, Germany;Division of Translational Surgical Oncology (TSO), National Center for Tumor Diseases (NCT/UCC) Dresden, Fetcherstr. 74, 01039, Dresden, Germany;

关键词: Edge detection;    Skeletonize algorithm;    Computational optimization;    Post-processing;   

DOI  :  10.1186/s12880-021-00650-z

来源: Springer

【 摘 要 】

BackgroundObject detection and image segmentation of regions of interest provide the foundation for numerous pipelines across disciplines. Robust and accurate computer vision methods are needed to properly solve image-based tasks. Multiple algorithms have been developed to solely detect edges in images. Constrained to the problem of creating a thin, one-pixel wide, edge from a predicted object boundary, we require an algorithm that removes pixels while preserving the topology. Thanks to skeletonize algorithms, an object boundary is transformed into an edge; contrasting uncertainty with exact positions.MethodsTo extract edges from boundaries generated from different algorithms, we present a computational pipeline that relies on: a novel skeletonize algorithm, a non-exhaustive discrete parameter search to find the optimal parameter combination of a specific post-processing pipeline, and an extensive evaluation using three data sets from the medical and natural image domains (kidney boundaries, NYU-Depth V2, BSDS 500). While the skeletonize algorithm was compared to classical topological skeletons, the validity of our post-processing algorithm was evaluated by integrating the original post-processing methods from six different works.ResultsUsing the state of the art metrics, precision and recall based Signed Distance Error (SDE) and the Intersection over Union bounding box (IOU-box), our results indicate that the SDE metric for these edges is improved up to 2.3 times.ConclusionsOur work provides guidance for parameter tuning and algorithm selection in the post-processing of predicted object boundaries.

【 授权许可】

CC BY