Abstract Capturing accurate representations of musculoskeletal system morphology is a core aspect of musculoskeletal modelling of the upper limb. Measurements of important geometric parameters such as the thickness of muscles and tendons are key descriptors of the underlying morphology. Though the measurement of those parameters can be estimated manually using cadaveric measurements, this is not an appropriate technique for constructing a personalised musculoskeletal model for an individual. Therefore, this work proposes and applies a novel method for evaluating the geometric parameters of the upper extremity based on automated ultrasound image analysis. The proposed algorithm involves advanced techniques from artificial intelligence and image processing to outline the necessary details of the musculoskeletal morphology from appropriately enhanced ultrasound images. The ultrasound images were collected from 25 healthy volunteers from different parts of upper limb. The results were compared with measurements of a manual evaluation. Our results showed that the average discrepancy between the manual and automatic measures of triceps thickness is 0.115 mm. This represents improved accuracy compared to several current approaches.