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PeerJ. 2018 Mar 15;6:e4411. doi: 10.7717/peerj.4411. eCollection 2018.

Detection of linear features including bone and skin areas in ultrasound images of joints.

Author information

1
Research & Development Center, Polish-Japanese Academy of Information Technology, Warsaw, Poland.
2
Institute of Informatics, Silesian University of Technology, Gliwice, Poland.
3
Department for Neurology, Rheumatology and Physical Medicine, Helse Førde, Førde, Norge.
4
Faculty of Engineering and Science, Western Norway University of Applied Sciences, Norway.
5
DIVE IN AI, Wroclaw, Poland.

Abstract

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the 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.

KEYWORDS:

Linear detector; Machine learning; Medical imaging; Synovitis

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