No abstract

It is important to measure scars in forensic and clinical medicine. In practice, scars are mostly manually measured and the results are diverse and influenced by various subjective factors. With the development of digital image technology and artificial intelligence, non-contact and automatic photogrammetry has been gradually used in some practical applications. In this article, we propose an automatic method for measuring the length of linear scars based on multi-view stereo (MVS) and deep learning, which combines the three-dimensional (3D) reconstruction algorithm of structure from motion (SfM) and the image segmentation algorithm based on a convolutional neural network. With a few pictures taken by a smart phone, automatic segmentation and measurement of scars can be realized. The reliability of the measurement was first demonstrated through simulation experiments on five artificial scars, giving errors of length below 5%. Then experiment results on 30 clinical scar samples showed that our measurements were in high agreement with manual measurements, with an average error of 3.69%. Our study demonstrates the application of photogrammetry in scar measurement is effective and the deep learning technique can realize the automation of scar measurement with high accuracy.