Shape Analysis of Erythrocytes using Mean Shift Segmentation

Shape Analysis of Erythrocytes using Mean Shift Segmentation

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-12 Number-1                          
Year of Publication : 2014
Authors : Umed Singh , Narender Singh
  10.14445/22315381/IJETT-V12P209

Citation 

Umed Singh , Narender Singh. "Shape Analysis of Erythrocytes using Mean Shift Segmentation", International Journal of Engineering Trends and Technology (IJETT), V12(1),45-49 June 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Blood disorders can cause morphological changes in mature red blood cells so by investigating blood smears morphologically, we can study and distinguish blood diseases such as anemia. In this paper we have taken the different red blood cell images and focused on extracting several features relating to shape, their circularity and elongation with the help of a decision logic all those various types of red blood cells were classified into 5 categories. According to the obtained results, diagnosis of blood disorders such as iron deficiency anemia, the anemia of chronic disease, ?-thalassemia trait, sickle cell anemia has been obtained. We have segmented the corpuscles using mean shift technique and otus’s method is used for binarization. We then have successfully extracted the contours and then traversed the contour for extracting the four parameters –diameter, area, shape geometric factor(sgf) and deviation value(dv) and done the classification.

References

[1] Andy Tsai*, Anthony Yezzi,” A Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets” IEEE transactions on medical imaging, vol. 22, no. 2, February 2003
[2] C. igathinathane et al,” Shape identification and particles size distribution from basic shape parameters using ImageJ”, 2008 Elsevier B.V.
[3] D. Karaku?, A. H. Onur, A.H. Deliormanl?, G. Konak,” Size and shape analysis of mineral particles using image processing technique”, The Journal of ORE DRESSING ® 2010.
[4] Dorin Comaniciu, Peter Meer, “Mean Shift: A Robust Approach towards Feature Space Analysis”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, May 2002.
[5] Gonzales : Digital Image Processing, Prentice Hall (3th Edition.)
[6] Kuo - Lung Wu and Miin - Shen Yang,”Mean Shift based Clustering” Journal Pattern Recognition Vol 40,Issue 11 ,Nov 2007
[7] Navin D. Jambhekar, “Red Blood Cells Classification using Image Processing” Science Research Reporter, Nov 2011,pp 151-154
[8] Vinod Kumar Katiyar, Demeke Fisseha,” Analysis of Mechanical Behavior of Red Blood Cell Membrane with Malaria Infection ”,World Journal of Mechanics, 2011, 1, pp 100-108.
[9] W. Wang and J. Paliwal.” Separation and identification of touching kernels and dockage components in digital images”, Volume 48, 2006.
[10] Ziegler, Anne Greet Bittermann, Mathias, ”Basic introduction to image processing.”, http://www.zmb.uzh.ch/resources/download/image_processing.pdf.

Keywords

Segmentation, Anemia, SGF, DV