Fully automated segmentation and morphometrical analysis of muscle fiber images

Authors


  • Parts of the method described in this paper were presented at the Annual Meeting of the Reference Centre for Neuromuscular Diseases of the German Society of Neuropathology and Neuroanatomy (DGNN) in April 2005, Aachen, Germany as well as at the Bildverarbeitung für die Medizin (BVM) 2006 Workshop, March 2006, Hamburg, Germany.

Abstract

Background:

Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections.

Methods:

The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts.

Results:

The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences.

Conclusions:

The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided. © 2007 International Society for Analytical Cytology

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