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.

• SP. Chokkalingam
• K. Komathy

• Computer Aided Diagnosis
• Edge Detection
• Histogram Smoothing
• Rheumatoid Arthritis.

[1] 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.
[2] 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.
[3] Anoraganinrum, "Cell Segmentation with Median Filter and
Mathematical Morphology Operation”, International Conference on
Image Analysis and Processing, pp. 1043-1046, 1999.
[4] 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.
[5] 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.
[6] ErmaiXie, T., M., McGinnity, QingXiang, Wu, ”Automatic Extraction
of Shape Features for Classification of Leukocytes”, International
Conference on Artificial Intelligence and Computational Intelligence,
2010.
[7] http://minitab.en.softonic.com.
[8] Krishnapuram, R., and J. M., Keller, "A possibilistic approach to
clustering,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp. 98-
110, 1993.
[9] 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.
[10] 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.
[11] SubrajeetMohapatra, DiptiPatra, and SanghamitraSatpathi, "Image
Analysis of Blood Microscopic Images for Acute Leukemia Detection”.
International Conference on Industrial Electronics Control and
Robotics, 2010.
[12] SubrajeetMohapatra, DiptiPatra, and Kundan Kumar, "Blood
Microscopic Image Segmentation using Rough Sets”, International
Conference on Image Information Processing ,2010.
[13] 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.
[14] Tycko, D.H., S., Anbalagan, H.C., Liu, and L., Ornstein, "Automatic
leukocyte classification using cytochemically stained smears," J
HistochemCytochem. pp. 178-94, 1976.
[15] 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.
[16] www.cs.waikato.ac.nz/ml/weka
[17] www.cs.utexas.edu/users/ml/tutorials/Weka-tut
[18] www.ehow.com "rheumatoid arthritis can cause low lymphocytes”
[19] www.minitab.com/en-US/minitab-statistical-software-40-years.aspx
[20] 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.