Research On Automatic Measurement Of Muscle Morphological Characteristics Using Ultrasonography

Abstract:Ultrasonography is being widely used as a clinical and research tool for dynamic studies of the muscle during contraction and relaxation, since it’s real-time, widely available, radiation-free and low-cost. Muscle architectural characteristics, such as fiber orientation, fascicle length, cross-sectional area and muscle thickness, can be extracted from ultrasonography to evaluate the muscle function and activity. And changes of these architectural parameters over the time can form quantitative observations of muscle behavior under contraction. This paper focused on the automatic measurement of the muscle thickness and fiber orientation in ultrasonography.Muscle thickness measurement in ultrasonography was traditionally conducted by a trained operator, and the manual detecting process is time-consuming and subjective. In recent years, some automatic methods have been proposed for estimating muscle thickness. However, these methods are limited to the estimation of muscle thickness at one or several specific locations. In this paper, we proposed an automatic tracking strategy to achieve the continuous and quantitative measurement for gastrocnemius muscle thickness in ultrasound images. The method involved three steps, tracking of seed points, contours extraction of aponeuroses and muscle thickness estimation. The performance of the algorithm was evaluated using500frames of ultrasound images. It was demonstrated in the experiments that the proposed methods could be used for objective tracking of aponeuroses and estimation of muscle thickness along the entire contours of aponeuroses in musculoskeletal ultrasound images.The traditional manual method for muscle fiber orientation (MFO) estimation in sonograms was labor-intensive and subjective. The automatic methods proposed in recent years also involved voting procedures which were computationally expensive. In this paper, we proposed a new framework to efficiently estimate MFO in sonograms. We firstly employed Multi-scale Vessel Enhancement Filtering to enhance fascicles in the sonograms and then the enhanced images were binarized. Finally, line-shaped patterns in the binary map were detected one by one, according to their shape properties. The performance of the algorithm was evaluated using45sonograms from three healthy adult male subjects and256frames from an aged subject with cerebral infarction respectively. Results of the experiments suggested that, without compromising the accuracy, in the proposed framework the previous orientation estimation algorithm was accelerated by reduction of its dependence on voting procedures.