Leveraging Computerised Automation for Long-Term Monitoring of Total Hip Replacements | Chapter 4 | Engineering Research: Perspectives on Recent Advances Vol. 8

 

Total Hip Replacement (THR) has significantly improved patients' mobility and quality of life by alleviating hip pain. However, long-term implant success is influenced by various factors that may lead to joint instability or failure. Early detection of such issues is crucial for effective patient care and intervention.

 

This study explores the application of image recognition algorithms for analysing radiographs to accurately assess joint stability in individuals who have undergone THR. By leveraging advanced image processing techniques, this approach enables continuous monitoring of implant integrity. Despite improvements in healthcare access, patient attrition after two years remains a challenge. The integration of Computerised Automated Machines (CAMs) can enhance patient screening, efficiently identifying those in need of further medical evaluation. By combining automation with precise diagnostic capabilities, CAM technology presents a promising solution for optimising post-THR assessments, streamlining healthcare operations, and improving long-term patient outcomes.

 

Author (s) Details

Sandhya Tatekalva
Department of Computer Science, S.V. University, Tirupati, Andhra Pradesh, India.

 

J. Rohan Theertha Reddy
SR, MS in Orthopedics, MediCiti Institute of Medical Sciences, Hyderabad, India.

 

M. Usha Rani
Department of Computer Science, S.P.M.V.V., Tirupati, Andhra Pradesh, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v8/5677

Two State of Art Image Segmentation Approaches | Chapter 8 | Current Topics on Mathematics and Computer Science Vol. 11

The fundamental purpose of this research is to use only a few general features of real-world objects to define object borders in outside scenes of images. In this scenario, segmentation and recognition should be handled as an interleaving technique rather than being separated. The purpose of this research is to provide an adaptive global clustering algorithm that can detect non-accidental structural relationships between the constituent parts of structured objects with multiple constituent parts. Background components such as the sky, tree, and ground are likewise distinguished using colour and texture information. This method sorts things into groups based on their attributes, and it doesn't require any prior knowledge of the items. The proposed method beat two state-of-the-art image segmentation approaches on two tough outside databases and in distinct outside natural scene situations, enhancing segmentation quality. Using this clustering approach, it is possible to avoid considerable reflection and excessive segmentation. The goal of this project is to improve performance and backdrop recognition capabilities.

Author (S) Details

P. Jenopaul

Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India.

Ranjeesh R. Chandran

Department of Robotics and Automation, Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India.

H. Shihabudeen

Department of Electronics and communication Engineering, College of Engineering Kidangoor, Kerala, India.

P. Anitha

Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India.

Anna Baby

Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India.

View Book :- stm.bookpi.org/CTMCS-V11/article/view/3982