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

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.

A Novel Method of Synovitis Stratification in Ultrasound Using Machine Learning Algorithms: Results From Clinical Validation of the MEDUSA Project.

Ultrasound is widely used in the diagnosis and follow-up of chronic arthritis. We present an evaluation of a novel automatic ultrasound diagnostic tool based on image recognition technology. Methods used in developing the algorithm are described elsewhere. For the purpose of evaluation, we collected 140 ultrasound images of metacarpophalangeal and proximal interphalangeal joints from patients with chronic arthritis. They were classified, according to hypertrophy size, into four stages (0-3) by three independent human observers and the algorithm. An agreement ratio was calculated between all observers and the standard derived from results of human staging using κ statistics. Results was significant in all pairs, with the highest p value of 3.9 × 10-6. κ coefficients were lower in algorithm/human pairs than between human assessors. The algorithm is effective in staging synovitis hypertrophy. It is, however, not mature enough to use in a daily practice because of limited accuracy and lack of color Doppler recognition. These limitations will be addressed in the future.