Research Article | OPEN ACCESS
Classification and Segregation of Abnormal Lymphocytes through Image Mining for Diagnosing Rheumatoid Arthritis Using Min-max Algorithm
1S.P. Chokkalingam and 2K. Komathy
1Department of Information Technology, Saveetha School of Engineering, Saveetha University
2Department of Computer Science and Engineering, Easwari Engineering College,
Anna University, Chennai, India
Research Journal of Applied Sciences, Engineering and Technology 2014 18:3926-3934
Received: December 18, 2013 | Accepted: January 01, 2014 | Published: May 10, 2014
Abstract
Advances in the acquisition of complex medical images and storing it for further analysis through image mining have significantly helped to identify the root causes for various diseases. Mining of medical image data set such as scanned images or blood cell images require extraction of implicit knowledge from the data set through hierarchical image processing techniques and identifying the relationships and patterns that are not explicitly stored in a single image. Rheumatoid Arthritis (RA) is an autoimmune disease and it cause chronic inflammation of the joints. Causes of the RA is unknown due to that need to find out in the early stage is required. Diagnosis of RA based on blood cell types and shapes requires computational analysis. An assistive technology for the doctor to detect and investigate rheumatoid arthritis is therefore required. The objective of the proposed work is to analyze the shapes of lymphocytes, a key component of blood cells that causes RA complications, to automate the process of identifying abnormal lymphocytes by estimating the centroids of lymphocytes using AIT centroid technique and thereby finding a differential count. The process involves cropping nucleus from the blood cell image, segmenting it and to investigate further whether the shapes of the lymphocytes are irregular and dissimilar. Features are extracted from each cell components for comparison and the abnormal lymphocytes are segregated from the normal. To enhance the segregation process, neural network based perceptron classifier tool is used.
Keywords:
Centroid analysis, image mining, lymphocytes, perceptron classifier, Rheumatoid Arthritis (RA), segmentation, white blood cells,
References
-
Agrawal, M. and D. Doermann, 2009. Clutter noise removal in binary document images. Proceeding of 10th International Conference on Document Analysis and Recognition (ICDAR '09), pp: 556-560.
CrossRef
-
Al-amri, S.S., N.V. Kalyankar and S.D. Khamitkar, 2010. A comparative study of removal noise from remote sensing image. IJCSI Int. J. Comput. Sci., 7(1).
-
Anoraganinrum, D., 1999. Cell segmentation with median filter and mathematical morphology operation. Proceeding of the International Conference on Image Analysis and Processing, pp: 1043-1046.
CrossRef
-
Ari?n, A.Z. and A. Asano, 2006. Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recogn. Lett., 27(2006): 1515-1521.
CrossRef
-
Bharanidharan, T. and D.K. Ghosh, 2012. A two dimensional image classification neural network for medical images. Eur. J. Sci. Res., 74(2): 286-291.
-
Brunelli, R. and O. Mich, 2000. Image retrieval by examples. IEEE T. Multimedia, 2: 164-71.
CrossRef
-
Kavallieratou, E. and E. Stamatatos, 2006. Improving the quality of degraded document images. Proceedings of the 2nd International Conference on Document Image Analysis for Libraries (DIAL'06), pp: 10-349.
CrossRef
-
Mamatha, H.R., S. Madireddi and K.S. Murthy, 2012. Performance analysis of various filters for De-noising of handwritten Kannada documents. Int. J. Comput. Appl., 48(12): 0975-888.
-
Murali, Y., M. Babu, M.V. Subramanyam and M.N. Giri Prasad, 2012. PCA based image denoising. SIPIJ, Vol. 2.
-
Nichol, J.E. and V. Vohra, 2004. Noise over water surfaces in Landsat TM images. Int. J. Remote Sens., 25(11): 2087-2093.
CrossRef
-
Nosrati, M., R. Karimi and M. Hariri, 2012. Detecting circular shapes from areal images using median filter and CHT. Global J. Comput. Sci. Technol., Vol. 12.
-
Padmavathi, G., P. Subashini, M. Muthu Kumar and S.K. Thakur, 2010. Comparison of filters used for underwater image-preprocessing. IJCSNS Int. J. Comput. Sci. Network Secur., 10(1).
-
Patidar, P., M. Gupta, S. Srivastava and A.K. Nagawat, 2010. Image de-noising by various filters for different noise. Int. J. comput. Appl., 9(4).
CrossRef
-
Sharma, A.K. and S. Sahni, 2011. A comparative study of classification algorithms for spam email data analysis. Int. J. Comput. Sci. Eng. (IJCSE), 3(5).
-
Singh, P. and H. Singh, 2012. A comparison of high pass spatial filters using measurements and automation. Int. J. Eng. Res. Technol. (IJERT), 1(3), ISSN: 2278-0181.
-
Subrajeet, M., D. Patra and S. Satpathi, 2010. Image analysis of blood microscopic images for acute leukemia detection. Proceeding of International Conference on Industrial Electronics Control and Robotics.
-
Sumathi, P. and G. Ravindran, 2011. The performance of fractal image compression on different imaging modalities using objective quality measures. Int. J. Eng. Sci. Technol. (IJEST), 3(1): 525-530.
-
Theerapattanakul, J., J. Plodpai, S. Mooyen and C. Pintavirooj, 2004. Classification of White Blood Cell using Adaptive Active Contour. ICCAS, Bangkok, Thailand, pp: 1892-1894.
-
Yampri, P., C. Pintavirooj, S. Daochai and S. Teartulakarn, 2006. White blood cell classification based on the combination of Eigen cell and parametric feature detection. Proceeding of 1st IEEE Conference on Industrial Electronics and Applications, pp: 1-4.
CrossRef
-
Zang, P. and L. Chen, 2001. Document filters using morphological and geometrical features of characters. Image Vision Comput., 19: 847-855.
CrossRef
Competing interests
The authors have no competing interests.
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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