Automated regional analysis of B-mode ultrasound images of skeletal muscle movement

Abstract

To understand the functional significance of skeletal muscle anatomy, a method of quantifying local shape changes in different tissue structures during dynamic tasks is required. Taking advantage of the good spatial and temporal resolution of B-mode ultrasound imaging, we describe a method of automatically segmenting images into fascicle and aponeurosis regions and tracking movement of features, independently, in localized portions of each tissue. Ultrasound images (25 Hz) of the medial gastrocnemius muscle were collected from eight participants during ankle joint rotation (2° and 20°), isometric contractions (1, 5, and 50 Nm), and deep knee bends. A Kanade-Lucas-Tomasi feature tracker was used to identify and track any distinctive and persistent features within the image sequences. A velocity field representation of local movement was then found and subdivided between fascicle and aponeurosis regions using segmentations from a multiresolution active shape model (ASM). Movement in each region was quantified by interpolating the effect of the fields on a set of probes. ASM segmentation results were compared with hand-labeled data, while aponeurosis and fascicle movement were compared with results from a previously documented cross-correlation approach. ASM provided good image segmentations (<1 mm average error), with fully automatic initialization possible in sequences from seven participants. Feature tracking provided similar length change results to the cross-correlation approach for small movements, while outperforming it in larger movements. The proposed method provides the potential to distinguish between active and passive changes in muscle shape and model strain distributions during different movements/conditions and quantify nonhomogeneous strain along aponeuroses.

Redundancy or heterogeneity in the electric activity of the biceps brachii muscle? Added value of PCA-processed multi-channel EMG muscle activation estimates in a parallel-fibered muscle

“…Estimates of muscle activation based on conventional bipolar EMG, however, have limitations (Farina et al, 2004;Staudenmann et al, 2006). Importantly, activity can be heterogeneous within muscles during voluntary contractions, as was shown with multi-channel EMG (Eriksson et al, 1984;Holtermann et al, 2005;Staudenmann et al, 2005Staudenmann et al, , 2009avan Zuylen et al, 1988, Vieira et al, 2011Wolf et al, 1998), as well as with ultrasound imaging (Bojson-Moller et al, 2004;Darby et al, 2012;Finni et al, 2003;Pappas et al, 2002). Thus information obtained by a single bipolar EMG will not be representative for the entire muscle and consequently muscle activation estimates derived from multi-channel EMG (collecting from different parts of the muscle) are superior to those derived from a single bipolar EMG (Staudenmann et al, 2010).…”

A New Method for Automatically Labeling Aircrafts in Airport Video

“…Gaussian Mixture Model [14,15] is used to establish a background model to extract background image and make difference with the original image to obtain a difference imaging, so as to detect the image moving target. KLT algorithm is used to track the image moving target [3][4][5][6][7]. KLT is a tracking algorithm based on feature points, which conducts multiple iterations through building a Gaussian pyramid and predicts the withdrawn feature points, so as to achieve the purpose of tracking.…”

“…The background picture is extracted through establishing a background model using Gaussian Mixture Model [14,15], and is compared with the original image to obtain a difference image, thus detecting the moving objects in image. The tracking of moving objects in image applies KLT algorithm [3][4][5][6][7]. KLT is a tracing algorithm based on feature points.…”

Real time identification of active regions in muscles from high density surface electromyogram

“…However, this is an invasive method, requiring to use many needles to properly investigate different regions. A non-invasive possibility is investigating the activity of muscle regions with ultrasounds [65] while recording the EMG with a system of electrodes transparent to ultrasounds [22]. Also in this case, the resolution of the identification of the muscle activity could be a limitation.…”