Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images
Advances in the field of image processing envision a new era of evaluation techniques and application of procedures in various different fields. One such field being considered is the biomedical field for prognosis as well as diagnosis of diseases. This plethora of methods though provides a wide range of options to select from, it also proves confusion in selecting the apt process and also in finding which one is more suitable. Our objective is to use a series of techniques on bone scans, so as to detect the occurrence of rheumatoid arthritis (RA) as accurately as possible. Amongst other techniques existing in the field our proposed system tends to be more effective as it depends on new methodologies that have been proved to be better and more consistent than others. Computer aided diagnosis will provide more accurate and infallible rate of consistency that will help to improve the efficiency of the system. The image first undergoes histogram smoothing and specification, morphing operation, boundary detection by edge following algorithm and finally image subtraction to determine the presence of rheumatoid arthritis in a more efficient and effective way. Using preprocessing noises are removed from images and using segmentation, region of interest is found and Histogram smoothing is applied for a specific portion of the images. Gray level co-occurrence matrix (GLCM) features like Mean, Median, Energy, Correlation, Bone Mineral Density (BMD) and etc. After finding all the features it stores in the database. This dataset is trained with inflamed and noninflamed values and with the help of neural network all the new images are checked properly for their status and Rough set is implemented for further reduction.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094341Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1999
 Aman Kumar Sharma and SuruchiSahni, "A Comparative Study of Classification Algorithms for Spam Email Data Analysis”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3, No 5, 2011.
 Anache, N., "Medical image analysis a challenge for computer vision research," Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on , vol.2, no., pp.1255,1256 vol.2, 16-20 Aug 1998.
 Anoraganinrum, "Cell Segmentation with Median Filter and Mathematical Morphology Operation”, International Conference on Image Analysis and Processing, pp. 1043-1046, 1999.
 Bharanidharan, T., and D.K., Ghosh, 2012. "A Two Dimensional Image classification Neural Network for Medical Images”, European Journal of Scientific Research,Vol.74 No.2 (2012), pp. 286-291.
 ChokkalingamS.P and K. Komathy, 2014. Classification and Segregation of Abnormal Lymphocytes through Image Mining for Diagnosing Rheumatoid Arthritis Using Min-max Algorithm. Research Journal of Applied Sciences, Engineering and Technology, 7(18): 3926- 3934.
 ErmaiXie, T., M., McGinnity, QingXiang, Wu, ”Automatic Extraction of Shape Features for Classification of Leukocytes”, International Conference on Artificial Intelligence and Computational Intelligence, 2010.
 Krishnapuram, R., and J. M., Keller, "A possibilistic approach to clustering,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp. 98- 110, 1993.
 NiponTheera-Umpon and SompongDhompongsa, "Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification”. IEEE Trans.Infor.Tech.Bio.Medi, Vol. 11, No. 2, 2007.
 Pavlova, P.E., K.P., Cyrrilov, and I. N., Moumdjiev, "Application of HSV colour system in the identification by colour of biological objects on the basis of microscopic images," Computerized Medical Imaging and Graphics 20(5), 1996, pp.357-64.
 SubrajeetMohapatra, DiptiPatra, and SanghamitraSatpathi, "Image Analysis of Blood Microscopic Images for Acute Leukemia Detection”. International Conference on Industrial Electronics Control and Robotics, 2010.
 SubrajeetMohapatra, DiptiPatra, and Kundan Kumar, "Blood Microscopic Image Segmentation using Rough Sets”, International Conference on Image Information Processing ,2010.
 Tabrizi P.R, S.H Rezatifighi and M.J. Yazdanpanah, "Using PCA and LVQ Neural Network for Automatic Recognition ofFive Types of White Blood Cells”, Inter. Conf. of the IEEE EMBS Buenos Aires, Argenti, 2010.
 Tycko, D.H., S., Anbalagan, H.C., Liu, and L., Ornstein, "Automatic leukocyte classification using cytochemically stained smears," J HistochemCytochem. pp. 178-94, 1976.
 Wei Xiong, Sim-HengOng, joo-Hwee Lim, Kelvin Weng Chiong Foong, Jiang Liu, Daniel Racoceanu, Alvin G.L., Chong and Kevin S.W., and Tan, "Automatic area classification in peripheral blood smears”, IEEE Trans.Bio.Engg, Vol.57, No. 8, 2008.
 www.ehow.com "rheumatoid arthritis can cause low lymphocytes”
 Yampri, P., C.Pintavirooj, S., Daochai and S., Teartulakarn, "White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection”, IEEE Conference on Industrial Electronics and Applications, 2006.