Fully automated segmentation and morphometrical analysis of muscle fiber images.
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.
Kim
Yoo-Jin
YJ
Institute of Neuropathology, Saarland University, School of Medicine, Homburg/Saar, Germany. yoo.jin.kim@uniklinik-saarland.de
Brox
Thomas
T
Feiden
Wolfgang
W
Weickert
Joachim
J
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008954:Models, Biological; D018485:Muscle Fibers, Skeletal / Q000648:ultrastructure*