Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning RJ Hemalatha Sathyabama University, Chennai, India https://orcid.org/0000-0003-4712-7482 V Vijaybaskar Department of Electronics and Telecommunication Engineering, Sathyabama University, Chennai, India TR Thamizhvani Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, India

The pace of population aging is growing faster worldwide. The quality of life of the aging population is mostly affected by rheumatic diseases. With the increasing rate of rheumatoid arthritis in the aging population, technological advances in the field of automatic image processing and analysis have paved way for automatic detection and diagnosis of arthritis based on how the grade of the synovial region is designed. The proposed method is based on spatial analysis using intensity-based approach to segment the skin border, thresholding and connectivity algorithm for bone region segmentation, hit-or-miss transform for bone line segmentation and distance measure with image profile to detect the joint region. After this process of localization, the synovial region is determined using the active contour technique. In arthritis condition, synovitis also occurs which is categorized into four different grades based on the fluid expansion in the synovial region. The different grades are defined and analyzed through deep learning. Convolutional neural network in a deep learning algorithm is used to diagnose the particular grade of synovitis to describe the nature of arthritis. With these results, a module to detect the nature of arthritis automatically is defined.
04 24 2019 06 2019 657 667 10.1177/0954411919845747 2 10.1177/sage-journals-update-policy journals.sagepub.com true https://journals.sagepub.com/page/policies/text-and-data-mining-license 10.1177/0954411919845747 https://journals.sagepub.com/doi/10.1177/0954411919845747 https://journals.sagepub.com/doi/pdf/10.1177/0954411919845747 https://journals.sagepub.com/doi/pdf/10.1177/0954411919845747 https://journals.sagepub.com/doi/full-xml/10.1177/0954411919845747 Diet, nutrition and the prevention of chronic diseases: report of a joint WHO/FAO Expert Consultation on Diet 2002 10.1371/journal.pone.0056008 10.1016/S0140-6736(03)15268-3 10.3899/jrheum.080675 Bullet World Health Organ Woolf AD 646 81 2003 10.1002/1529-0131(200012)43:12<2762::AID-ANR16>3.0.CO;2-# Rev Brasil Reumatol Arend CF. 88 53 1 2013 Swiss Med Week Schett G. 142 2012 10.7717/peerj.4411 Stud Inform Popowicz A 7 37 3 2016 P SPIE Nurzynska K 1 9897 2016 Proceedings of the international joint conference on neural networks Moran MBH 10.1117/1.JBO.23.7.071205 Bullet Polish Acad Sci Radlak K 235 66 2 2018 10.1007/s00256-003-0693-2 Proceedings of the computational vision and medical image Radlak K 215 10.1016/j.joca.2016.03.004 10.1016/j.ultrasmedbio.2017.10.005 Proceedings in the international conference on computer vision and graphics Kulbacki M 364 10.1002/rcs.160 Biomedicine Hemalatha RJ 101 37 1 2017 Int J Comput Elect Auto Control Inform Eng Elrajubi OM 993 8 6 2014 10.1002/0471221325 10.1016/S0734-189X(86)80047-0 10.1155/2018/4976372 J Clin Diagnost Res Hemalatha RJ 12 2018 10.1109/TMI.2016.2528162 10.1016/j.neunet.2017.10.001 10.1007/978-1-4842-2845-6_6 J Clin Diagnost Res Thamizhvani TR 11 12 9 2018 Int J Amb Syst Appl Saravanan K 11 2 4 2014 Proceedings of 2010 IEEE 17th international conference on image processing Dietenbeck T 665 10.1146/annurev-bioeng-071516-044442 10.1007/s11042-017-5449-4