{"title":"Accelerating Euclidean Distance Transforms: A Fast and Flexible Approach With Multi-Vendor GPU, Multi-Threading, and Multi-Language Support","authors":"Dale Black;Wenbo Li;Qiyu Zhang;Sabee Molloi","doi":"10.1109/ACCESS.2025.3548563","DOIUrl":null,"url":null,"abstract":"Euclidean distance transforms are fundamental in image processing and computer vision, with critical applications in medical image analysis and computer graphics. However, existing implementations often lack performance, flexibility, or cross-platform compatibility. This paper introduces a novel approach to accelerating Euclidean distance transforms using hardware-agnostic GPU acceleration, multi-threading, and cross-language support. Our method, implemented in Julia with Python bindings, supports multiple GPU platforms including NVIDIA CUDA, AMD ROCm, Apple Metal, and Intel oneAPI. Benchmarks demonstrate substantial performance improvements, achieving speedups by a factor of 250 for 2D and a factor of 400 for 3D transforms compared to optimized CPU implementations. We showcase the impact of our approach through two real-world applications: accelerating the Hausdorff distance loss function for medical image segmentation, achieving a 7.4-fold improvement in processing speed with enhanced accuracy, and enhancing a GPU-optimized distance transform-based skeletonization algorithm with performance gains up to a factor of 88. Our open-source implementation provides a flexible, high-performance solution for exact Euclidean distance transforms, advancing the state-of-the-art in medical image analysis and computer vision.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"44636-44649"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912438","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10912438/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Euclidean distance transforms are fundamental in image processing and computer vision, with critical applications in medical image analysis and computer graphics. However, existing implementations often lack performance, flexibility, or cross-platform compatibility. This paper introduces a novel approach to accelerating Euclidean distance transforms using hardware-agnostic GPU acceleration, multi-threading, and cross-language support. Our method, implemented in Julia with Python bindings, supports multiple GPU platforms including NVIDIA CUDA, AMD ROCm, Apple Metal, and Intel oneAPI. Benchmarks demonstrate substantial performance improvements, achieving speedups by a factor of 250 for 2D and a factor of 400 for 3D transforms compared to optimized CPU implementations. We showcase the impact of our approach through two real-world applications: accelerating the Hausdorff distance loss function for medical image segmentation, achieving a 7.4-fold improvement in processing speed with enhanced accuracy, and enhancing a GPU-optimized distance transform-based skeletonization algorithm with performance gains up to a factor of 88. Our open-source implementation provides a flexible, high-performance solution for exact Euclidean distance transforms, advancing the state-of-the-art in medical image analysis and computer vision.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.