Muscle histology image analysis for sarcopenia: registration of successive sections with distinct ATPase activity

  • Authors:

  • Olcay Sertel;Belma Dogdas;Chi Sung Chiu;Metin N. Gurcan

  • Affiliations:

  • Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, OR and Dept. of Biomedical Informatics, The Ohio State University, Columbus, OR and Dept. of Applied Computer Sci ...;Dept. of Applied Computer Science and Mathematics, Merck & Co., Inc., Rahway, NJ;Musculo-Skeletal Biology Program Team, Merck & Co., Inc., Boston, MA;Dept. of Biomedical Informatics, The Ohio State University, Columbus, OR

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

One way of evaluating muscle quality is to detennine its fiber type composition in histological sections. A complete muscle fiber type characterization system requires combining information from successive muscle histology images with different ATPase stain. Due to the local and global defonnations introduced in slide preparation process, a precise non-rigid registration is essential to construct the spatial correspondences between these successive images. This study proposes an approach for automated non-rigid registration of successive muscle histological sections. We propose a feature-based registration that uses a two stage approach: a rigid initialization followed by a non-rigid refinement. The rigid initialization step globally aligns successive tissue slides by finding correspondences between individually segmented muscle fibers using Fourier shape descriptors and computing the global rigid transformation using a voting scheme tolerant to mismatches. In the non-rigid stage we establish precise point correspondences using the nonnalized cross correlation metric and compute the non-rigid distortion using a polynomial transformation that minimizes the mean square distance between these control points.