Please use this identifier to cite or link to this item: doi:10.22028/D291-26340
Title: Fully automated segmentation and morphometrical analysis of muscle fibre images
Author(s): Kim, Yoo-Jin
Brox, Thomas
Feiden, Wolfgang
Weickert, Joachim
Language: English
Year of Publication: 2006
Free key words: automated morphometry
image analysis
muscle fibre size
DDC notations: 510 Mathematics
Publikation type: Other
Abstract: Background Measurement of muscle fibre size and determination of size distribution is important in the assessment of neuromuscular disease. Fibre 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 fibres 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 colour 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 fibres. Complete automation minimises user interaction, thus, batch processing, as well as objective and reproducible muscle fibre morphometry are provided.
Link to this record: urn:nbn:de:bsz:291-scidok-46648
Series name: Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Series volume: 177
Date of registration: 13-Mar-2012
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Mathematik
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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