Machine Learning Approaches in Developing Expert System for Medical Image Analysis with respect to THR: From Detection to Diagnosis
Main Article Content
Abstract
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. There is a need to find any deviation that can be acquired in position of artificial femur after the log time of surgery, well in advance thereby overcome the adverse socio economic and psychological burden to both the patient as well as the surgeon. The aim of the study is to develop a non-invasive, ultrasound-based, method of diagnosing acetabular cup loosening at an early stage before any major bone erosion has taken place. The proposed study will build on a previously successful technique for the diagnosis of loosing of the femoral stem component of a THR. This paper highlights the steps like box filter section, mainly first order and second order, followed by key points selection, key points extraction, key point matching and finally finding the deviation through motion vector analysis. The data for this research has been collected from different hospitals in Andhra Pradesh and Tamil Nadu.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
Marques, O. (2011). Practical image and video processing using MATLAB. Hoboken: Wiley-IEEE Press.
Sandhya, Tatekalva., Prof.Usha Rani, M., & Dr.Maruthi Krishna, M. (2017). Image Processing Techniques for Measuring the Acetabular Cup Orientation from Anteversion Angle of Revised Cemented Total Hip Arthroplasty (THA). International journal of Emerging Technology in Computer Science & Electronics (IJETCSE), ISSN: 0976-1353, volume 24, issue 1.
David G. Lowe. (2004). Distinctive Image Features from Scale-Invariant Key points. Computer Science Department University of British Columbia, Vancouver, B.C., Canada.
Marius, Muja., Radu. BogdanRusuy., Gary Bradskiy., & David Lowe. (2011). REIN - A Fast, Robust, Scalable Recognition Infrastructure. University of British Columbia, Canada. IEEE Xplore, published in IEEE International conference on Robotics and Automation (ICRA). DOI: 10.1109/ICRA.2011.5980153. e-ISBN: 978-1-61284-385-8.
T. Sandhya., & Prof. M. Usha Rani. (2016). CONTEMPORARY REVISION ANALYSIS OF THE HIP REPLACEMENT REGISTRIES IN INDIA. International Journal of Computational Science, Mathematics and Engineering. Volume-3-Issue-11-November-2016 ISSN-2349-8439
T. Sandhya., & Prof. M. Usha Rani. (2019). Need for Computerized Automated Machine (CAM) for finding THR issues in Patients. International Journal of Innovative Technology and Exploring Engineering (IJITEE). ISSN: 2278-3075,Volume-8, Issue-7C2.
S.J. Ferguson., J.T. Bryant., R. Ganz., & K. Ito. (2003). An in vitro investigation of the acetabularlabral seal in hip joint mechanics. Journal of Biomechanics. volume 36, issue 2, pages 171-178.
Joachim Pfeil., & W.E.Siebert. (2010). Minimally Invasive Surgery in Total Hip Arthroplasty. Heidelberg: Springer. Doi: https://doi.org/10/1007/978-3-642-00897-9.
Chen-Kun Liawa., Rong-SenYangb., Sheng-MouHoub., Tai-Yin Wuc., & Chiou-Shann. (2006). A Simple Mathematical Standardized Measurement of Acetabulum Anteversion after Total Hip Arthroplasty. ISSN 1748-670X print/ISSN 1748-6718 online.
G. Wu, S. Siegler., P. Allard., C. Kirtley., A. Leardini., D. Rosenbaum., M. Whittle., D. D. D’Lima., L. Cristofolini., H. Witte., O. Schmid., I. Stokes. (2002). ISB Recommendation on Definitions of Joint Coordinate System of Various Joints for the Reporting of Human Joint Motion-part I: Ankle, Sip, and Spine. Journal of Biomechanics, 2002, 35(4): 543-48.
C. Nikou., B. Jaramaz., A. M. DiGioia., & T. J. Levison. (2000). Description of Anatomic Coordinate System and Rationale for use in an Image-guided Total Hip Replacement System. Medical Image Computing and Computer-Assisted Intervention-MICCAI. Lecture Notes of Computer Science. Vol. 1935, pp. 1188-1194.
Rafael C, Gonzalez., & Richard E.Woods. (2008). Digital Image Processing. published by Pearson Edition, Inc. Publishing as Prentice Hall, copyright © 2008. ISBN: 978-81-317-2695-2.
Don, Fussell. (2010). Image Processing. University of Texas at Austin CS384G - Computer Graphics Fall.
Elaine Rich., & Kelvin Knight. (2009). Artificial Intelligence. TMH.
Fayyad U., Piatetsky-Shapiro G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, American Association for Artificial Intelligence.