Detection of linear features including bone and skin areas in ultrasound images of joints
- Published
- Accepted
- Subject Areas
- Orthopedics, Radiology and Medical Imaging, Rheumatology, Human-Computer Interaction, Computational Science
- Keywords
- Synovitis, Medical Imaging, Machine Learning, Linear Detector
- Copyright
- © 2018 Bąk et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Detection of linear features including bone and skin areas in ultrasound images of joints. PeerJ Preprints 6:e3519v1 https://doi.org/10.7287/peerj.preprints.3519v1
Abstract
Identifying the separate parts in ultrasound images such as bone and skin plays the crucial role in synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results.
Author Comment
This is a submission to PeerJ for review.