International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.8, No.1, Jan. 2016
IKRAI: Intelligent Knee Rheumatoid Arthritis Identification
Full Text (PDF, 516KB), PP.18-24
Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to grayscale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 95.5 %.
Cite This Paper
Abdulkader Helwan, David Preye Tantua, Emmanuel adeola,"IKRAI: Intelligent Knee Rheumatoid Arthritis Identification", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.1, pp.18-24, 2016. DOI: 10.5815/ijisa.2016.01.03
G. Navarro-Cano, S, Pogosian, J.F. Roldan, A. Escalante, “Association of mortality with disease severity in rheumatoid arthritis, independent of comorbidity”. Arthritis Rheum; 48(9): 2425-33.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73, 2003.
L. Shamir, S.M. Ling, W.W. Scott, A. Bos, N. Orlov, T.J. Macura, D.M. Eckley, L. Ferrucci, I.G. Goldberg,. “Knee X-Ray image Analysis Method for Automated Detection of Osteoarthritis”, IEEE Transaction on BIOMEDICAL engineering, vol. 56, No. 2, Feb 2009.
O. Emuoyibofarhe, K.F. Taiwo, “Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis,” I.J. Intelligent Systems and Applications, 4, 9, August 2012.
SP. Chokkalingam, K. Komathy, “Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images,” World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering, 8, 5, 2014.
Z. Wang, D., Wang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Trans. on circuits and Systems II: Analog and Digital signal processing , Vol. 46, no. I , pp. 78-80, 1999.
H. Abdulkader, “ITDS: Iris Tumor Detection System Using Image Processing Techniques,” International Journal of Science and Engineering Research, p.p.45-80, 2014.
R. Gonzalez, and E. Woods, Digital Image Processing, 2cd ed. Prentice-Hall, 2002.
A. Boujelben, H. Tmar, M. Abid, and J. Mnif, “Automatic Diagnosis of Breast Tissue,” Advances in Cancer Management, p.p. 258–2270, doi: 10.5772/22565, 2012.
S.S. Mokri, M.I. Saripan, M.H. Marhaban, A.J. Nordin, “Lung segmentation in CT for thoracic PET-CT registration through visual study,” in Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, Langkawi, p.p. 550 - 554, 17-19, Dec. 2012.
K. Adnan, E. Al-Zgoul, “Image Segmentation of Blood Cells in Leukemia Patients”. Recent Advances in Computer Engineering and Application, 2010 pp. 104–109.
K. Adnan, “IBCIS: Intelligent blood cell identification system”. Progress in Natural Science, (2008) 1309-1314, doi:10.1016.
M.O. Reza, M.F. Ismail, A.A. Rokoni, M.A.R. Sarkar, “Smart Parking System with Image Processing Facility,” I.J. Intelligent Systems and Applications, 3, p.p 41-47, DOI: 10.5815/ijisa.2012.03.06, 2012.
K. Anupama, C. Ashish, M. Deepak, G. Sachin , “COCOMO Estimates Using Neural Networks, ” I.J. Intelligent Systems and Applications, 9, pp. 22-28, DOI: 10.5815/ijisa.2012.09.03, 2012.