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Figure 7.10 Analysis of damaged muscle fibers using different thresholding strategies.
(A) An image of damaged fibers from a murine muscle injected with snake venom,
labeled for laminin, is displayed. Fiber boundary mask generated by CyteSeer
®
utilizing
the Savitsky-Golay and Otsu thresholding strategies are displayed in (B) and (C),
respectively.
Muscle Algorithm (
Fig. 7.10
) using different thresholding strategies. The
algorithm typically uses a Savitsky-Golay thresholding strategy, which yields
excellent recognition of fiber boundaries in sections from normal muscle.
However, for the damaged tissue, the fiber masks were inappropriately
“smoothed” compared to the actual fiber outlines. Substituting an Otsu-
based thresholding algorithm yielded much better recognition of the intri-
cate fiber outlines of the damaged tissue. Accordingly, several thresholding
options were incorporated into the CyteSeer
®
's Skeletal Muscle Algorithm
user interface, enabling researchers to optimize the algorithm for the variety
of muscle fiber patterns likely to be encountered in disease models. These
considerations also point toward a potentially novel analysis strategy, as
the fiber shape deviations that develop under pathological conditions can
be potentially quantified in an automated fashion.
7. FUTURE DIRECTIONS FOR AUTOMATED
QUANTITATIVE ANALYSIS OF SKELETAL MUSCLE
In the following sections, two subjects will be discussed which are
emerging in importance relevant to skeletal muscle research. The first of
which is the potential use of automated analysis of skeletal muscle biopsies
to assist in the quantification of fiber-specific mitochondrial function, which
has considerable implications in both research and medical diagnosis. The
second subject concerns a novel strategy of using the Human Protein Atlas
database for helping to define fiber-specific protein expression patterns in
human skeletal muscle.
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