Non-rigid registration for large sets of microscopic images on graphics processors

Antonio Ruiz, Manuel Ujaldon, Lee Alex Donald Cooper, Kun Huang*

*Corresponding author for this work

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K ×16K pixels) and breast cancer tumors (23K ×62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

Original languageEnglish (US)
Pages (from-to)229-250
Number of pages22
JournalJournal of Signal Processing Systems
Volume55
Issue number1-3
DOIs
StatePublished - Apr 1 2009

Keywords

  • Feature detection
  • Graphics processors
  • High-performance computing
  • Image registration and segmentation
  • Microscopic imaging
  • Pattern analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture

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