Purpose: Impaired insulin-induced microvascular recruitment in skeletal muscle contributes to insulin resistance in type 2 diabetic disease. Previously, quantification of microvascular recruitment at the capillary level has been performed with either the full image or manually selected region-of-interests. These subjective approaches are imprecise, time-consuming, and unsuitable for automated processes. Here, an automated multiscale image processing approach was performed by defining a vessel diameter threshold for an objective and reproducible analysis at the microvascular level.
Approach: A population of C57BL/6J male mice fed standard chow and studied at age 13 to 16 weeks comprised the lean group and 24- to 31-week-old mice who received a high-fat diet were designated the obese group. A clinical ultrasound scanner (Acuson Sequoia 512) equipped with an 15L8-S linear array transducer was used in a nonlinear imaging mode for sensitive detection of an intravascular microbubble contrast agent.
Results: By eliminating large vessels from the dynamic contrast-enhanced ultrasound (DCE-US) images (above 300 μm in diameter), obesity-related changes in perfusion and morphology parameters were readily detected in the smaller vessels, which are known to have a greater impact on skeletal muscle glucose disposal. The results from the DCE-US images including all of the vessels were compared for three different-sized vessel groups, namely, vessels smaller than 300, 200, and 150 μm in diameter.
Conclusions: Our automated image processing provides objective and reproducible results by focusing on a particular size of vessel, thereby allowing for a selective evaluation of longitudinal changes in microvascular recruitment for a specific-sized vessel group between diseased and healthy microvascular networks.
A novel algorithm for hierarchical multi-level image mosaicing for autonomous navigation of UAV is proposed.
The main contribution of the proposed system is the blocking of the error accumulation propagated along
the frames, by incrementally building a long-duration mosaic on the fly which is hierarchically composed of
short-duration mosaics. The proposed algorithm fulfills the real-time processing requirements in autonomous
navigation as follows. 1) Causality: the current output of the mosaicing system depends only on the current
and/or previous input frames, contrary to existing offline mosaic algorithms that depend on future input frames as
well. 2) Learnability: the algorithm autonomously analyzes/learns the scene characteristics. 3) Adaptability: the
system automatically adapts itself to the scene change and chooses the proper methods for feature selection (i.e.,
the fast but unreliable LKT vs. the slow but robust SIFT). The evaluation of our algorithm with the extensive
field test data involving several thousand airborne images shows the significant improvement in processing time,
robustness and accuracy of the proposed algorithm.