Tiulpin A., Thevenot J., Rahtu E., Saarakkala S. (2017) A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs. In: Sharma P., Bianchi F. (eds) Image Analysis. SCIA 2017. Lecture Notes in Computer Science, vol 10270. Springer, Cham
A novel method for automatic localization of joint area on knee plain radiographs
|Author:||Tiulpin, Aleksei1; Thevenot, Jerome1; Rahtu, Esa2;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
3Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902205800
|Publish Date:|| 2019-02-20
Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computer-aided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tackle the issue. Here, three different datasets of knee radiographs were used (n = 473/93/77) to validate the overall performance of the method. Our pipeline consists of two parts: anatomically-based joint area proposal and their evaluation using Histogram of Oriented Gradients and the pre-trained Support Vector Machine classifier scores. The obtained results for the used datasets show the mean intersection over the union equals to: 0.84, 0.79 and 0.78. Using a high-end computer, the method allows to automatically annotate conventional knee radiographs within 14–16 ms and high resolution ones within 170 ms. Our results demonstrate that the developed method is suitable for large-scale analyses.
Lecture notes in computer science
|Pages:||290 - 301|
Proceedings of the 20th Scandinavian Conference on Image Analysis, SCIA 2017; Tromso; Norway; 12 -14 June 2017
|Host publication editor:||
Scandinavian Conference on Image Analysis
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
MOST is comprised of four cooperative grants (Felson – AG18820; Torner – AG18832, Lewis – AG18947, and Nevitt – AG19069) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by MOST study investigators. The authors would also like to acknowledge the strategic funding of University of Oulu.
© Springer International Publishing AG 2017. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science, vol 10270.
The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-59129-2_25.