Early detection of joint abnormalities from ultrasound images


  • R J. Hemalatha
  • Dr V. Vijaybaskar
  • A Josephin Arockia Dhivya
  • . .






Ultrasound, Joint Abnormalities, Genetic Algorithm, Particle Swarm Optimization.


Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly





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How to Cite

J. Hemalatha, R., V. Vijaybaskar, D., Josephin Arockia Dhivya, A., & ., . (2018). Early detection of joint abnormalities from ultrasound images. International Journal of Engineering & Technology, 7(2.25), 105–108. https://doi.org/10.14419/ijet.v7i2.25.16569
Received 2018-07-30
Accepted 2018-07-30
Published 2018-05-03