Automatic myonuclear detection in isolatedsingle muscle fibers using robust ellipse fitting and sparse representation

Hai Su, Fuyong Xing, Jonah D. Lee, Charlotte A. Peterson, Lin Yang

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Accurate and robust detection of myonuclei in isolated single muscle fibers is required to calculate myonuclear domain size. However, this task is challenging because: 1) shape and size variations of the nuclei, 2) overlapping nuclear clumps, and 3) multiple z-stack images with out-of-focus regions. In this paper, we have proposed a novel automatic detection algorithm to robustly quantify myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first converted into one all-in-focus image using multi-focus image fusion. A sufficient number of ellipse fitting hypotheses are then generated from the myonuclei contour segments using heteroscedastic errors-in-variables (HEIV) regression. A set of representative training samples and a set of discriminative features are selected by a two-stage sparse model. The selected samples with representative features are utilized to train a classifier to select the best candidates. A modified inner geodesic distance based mean-shift clustering algorithm is used to produce the final nuclei detection results. The proposed method was extensively tested using 42 sets of z-stack images containing over 1,500 myonuclei. The method demonstrates excellent results that are better than current state-of-the-art approaches.

Original languageEnglish
Article number6674296
Pages (from-to)714-726
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume11
Issue number4
DOIs
StatePublished - 2014

Keywords

  • Robust ellipse fitting
  • muscle
  • segmentation
  • sparse optimization

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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