Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30132
Isolation and Classification of Red Blood Cells in Anemic Microscopic Images

Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.

Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096592

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2798

References:


[1] Organization (WHO), World Health. "Global Database on Anemia". The database on Anemia includes data by country on prevalence of anemia and mean hemoglobin concentration, (2010).
[2] Turgeon, Mary Louise, "Clinical Hematology: Theory and Procedures", Lippincott Williams & Wilkins, P. 100, (2012).
[3] Suzuki, Kenji. "A Review of Computer-Aided Diagnosis in Thoracic and Colonic Imaging". Quantitative imaging in medicine and surgery, Vol. 2, No. 3, P. 163, (2012).
[4] Adollah, R., Mashor, M.Y., Mohd Nasir, N.F., Rosline, H., Mahsin, H., Adilah, H. "Blood Cell Image Segmentation: A Review", In 4th Kuala Lumpur Internationals Conference on Biomedical Engineering, (2008).
[5] Hiremath, P. S., ParashuramBannigidad, and SaiGeeta, "Automated Identification and Classification of WhiteBlood Cells (Leukocytes) in Digital Microscopic Images", IJCA special issue on “Recent Trends in Image Processing and Pattern Recognition: RTIPPR, (2010).
[6] Chen, Hung-Ming, Ya-Ting Tsao, and Shin-Ni Tsai, "Automatic Image Segmentation and Classification Based on Direction Texton Technique for Hemolytic Anemia in
[7] Thin Blood Smears", Machine Vision and Applications, Vol. 25, No. 2, (2014).
[8] Aerkewar, Prafulla N., and G. H. Agrawal, "Image Segmentation Methods for Dermatitis Disease: A Survey", Image, Vol 2, Issue 1, Pp. 01-06, (2013).
[9] Baghli, Ismahan,"Hybrid framework Based on Evidence Theory for Blood Cell Image Segmentation", Medical Imaging, (2014).
[10] Roussev, Vassil, and Candice Quates, "File Fragment Encoding Classification - An Empirical Approach", Digital Investigation, Vol. 10, S69-S77, (2013).
[11] Yılmaz, Z. and M.R. Bozkurt, "Determination of Women Iron Deficiency Anemia Using Neural Networks", Journal of Medical Systems, Vol. 36, No. 5, Pp. 2941-2945, (2012).
[12] Jolliffe, Ian, "Principal Component Analysis", John Wiley & Sons, Ltd,. 2005.
[13] WEKA Available from: http://www.cs.waikato.ac.nz/ ~ml/weka
[14] Savkare, S. and S. Narote, "Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite's Life Stage", Procedia Technology, Vol. 6, Pp. 405-410, (2012).
[15] Lavesson, Niklas, "Evaluation and Analysis of Supervised Learning Algorithms and Classifiers", Blekinge Institute of Technology, 2006
[16] Rajendran, P. and M. Madheswaran, "An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure", Journal of Medical Systems, Vol. 36, No. 2, Pp. 747-764, (2012).