Early detection of joint abnormalities from ultrasound images

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    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

     

     

     

  • Keywords


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

  • References


      [1] Alison Noble. J and Djamal Boukerroui, “Ultrasound imag Ultrasound image Segmentation: A survey” , IEEE Transaction on Medical Imaging, Vol. 25 , No. 8, (2006), pp. 987-1010.

      [2] Amanpreet Kaur and Singh. M .D, “An Overview of PSO- based Approaches in Image Segmentation”, International Journal of Engineering and Technology (IJET), Vol. 2, No.8, (2012), pp. 2049-3444.

      [3] Anita Tandan, Rohit Raja and Yamini Chouhan, “Image Segmentation based on Particle Swarm Optimization Technique”, International Journal of Science, Engineering and Technology Research, Vol. 3 , No. 2, (2014), pp. 257-260.

      [4] Fenster. A and Downey. D. B, “Three dimensional Ultrasound Imaging: A review”, IEEE Transaction on Biomedical Engineering, Vol. 15 (1996), pp. 41-51.

      [5] Gopi Raju. N and Nageswara Rao. P. A, “Particle Swarm Optimization methods for Mammography”, International Journal of Engineering Research and Applications, Vol. 3, No. 6, (2013), pp. 1572-1579.

      [6] Jensen. J.R and Kalmesh Lulla, “Introductory to Digital Image Processing: A remote sensing perspective”, Journal of Geocarto International, Vol. 2, No. 1 (1987), pp. 65.

      [7] Kamaljeet Kaur, “Digital image processing in Ultrasound images”, International journal on Recent and Innovation trends in computing and communication, Vol. 1, (2013), pp. 388-393.

      [8] Komal. R. Hole, Prof. Vijay. S. Gulhane, and Nitin. D. Shellokar, “Application of Genetic Algorithm for Image Enhancement and Segmentation”, International Journal of Advanced Research in Computer Engineering and Technology, Vol. 2, (2013), pp. 1342-1346.

      [9] Lukas Krasula, Miloš Klíma, Eric Rogard and Edouard Jeanblanc, “MATLAB- based Application for Image processing and Image quality”, Radioengineering Vol. 21, No. 1, (2012), pp. 154-161.

      [10] Mantas Paulinas and Andrins Usinskas, “A Survey of Genetic Algorithm applications for Image Enhancement and Segmentation”, Information Technology and Control, Vol. 36, No. 3, (2007), pp. 278-284.

      [11] Mayr. J, Grechenig. W, Peichah and Tesch. N. P, “Ultrasonic Anatomy of Joints”, Der Orthopade, Vol. 31, No. 2, (2002), pp. 135-142.

      [12] Mohammad Talebi, Ahamd Ayatollahi and Ali Kermani, “Medical Ultrasound image Segmentation using Genetic active contour”, Journal of Biomedical science and Engineering , Vol. 4 , (2011), pp. 105-109.

      [13] Neculai Archip, Robert Rohling, Peter Cooperberg, Hamid Tahmasebpour and Simon K. Warfield, “Spectral clustering Algorithms for Ultrasound image Segmentation”, Springer – Verlag Berlin Heidelberg , Vol. 37, (2005), pp. 862-869.

      [14] Nikel Amoda and Ramesh .K. Kulkarni, “Image Segmentation and Detection using Watershed transform and Region based Image Retrieval”, International Journal of Emerging Trends and Technology in Computer science, Vol. 2, No. 2, (2013), pp. 89-94.

      [15] Hemalatha R.J., Vijayabaskar V. Despeckling Filter Evaluation Using Image Quality Metrics and Coefficient of Variation. In: Bhattacharyya P., Sastry H., Marriboyina V., Sharma R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, Springer, 2018, Jun 828(65).

      [16] Pedram Ghamsi., Micael. S. Couceiro., Jon Atli Benediktsson and Nuno. M. F. Ferreira, “An Efficient method for Segmentation of Images based on Fractional calculus and Natural selection”, Expert systems and Applications, Vol. 39, (2012), pp. 12407-12417.

      [17] Peyvandi. M., Zafarani. M and Nasr. E, “Comparison of Particle Swarm Optimization and Genetic Algorithm in the Improvement of Power system stability by SSSC-based controller”, Journal of Electrical Engineering and Technology , Vol. 6, No. 2, (2011), pp. 182-191.

      [18] Sigrid Pillen, “Skeletal Muscle Ultrasound”, European Journal Translational Myology , Vol. 1, No. 4, (2010), pp. 145-155.

      [19] Suzuki, Abe. K., Macmahon. H and Doi. K, “Image Processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network”, IEEE Transaction Medical Imaging, Vol. 24, No. 4, (2000), pp. 406-416.

      [20] Hemalatha.R.J and Dr.Vijayabaskar, “Ultrasonography For Rheumatic Diseases – A Review”, V2 Int J Pharm Bio Sci, Vol. 8, No. 2, (2017),pp.85-89

      [21] Hemalatha, R.J and Vijayabaskar. V, “Analysis of despeckling filter on rheumatoid arthritis affected ultrasound images”, Biomedicine (India), Vol. 37, No.1, (2017).


 

View

Download

Article ID: 16569
 
DOI: 10.14419/ijet.v7i2.25.16569




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.