Abstract
The diagnosis of a patient’s pathological condition, through the study of peripheral blood smear images, is a highly complicated process, the results of which require high levels of precision. In order to analyze the cells in the images individually, the cells can be segmented using appropriate automated segmentation techniques, thereby avoiding the cumbersome and error-prone existing manual methods. A marker controlled watershed transform, which was used in the previous study is an efficient technique to segment the cells and split overlapping cells in the image. However this technique fails to split the overlapping cells that do not have higher gradient values in the overlapping area. The proposed work aims to analyze the concavity of the overlapping cells and split the clumped Red Blood Cells (RBCs), as RBC segmentation is vital in diagnosing various pathological disorders and life-threatening diseases such as malaria. Splitting is done based on the number of dip points in the overlapping region using developed splitting algorithms. Successful splitting of overlapped RBCs help the count of the RBC’s remain accurate during the search for possible pathological infections and disorders.
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References
Fan, J., Zhang, Y., Wang, R., Li, S.: A separating algorithm for overlapping cell images. Journal of Software Engineering and Applications 6(4), 179–183 (2013)
Farhan, M., Yli-Harja, O., Niemistö, A.: A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recognition 46(3), 741–751 (2013)
Feminna, S., Robinson, T., Mammen, J.J., Thomas, H.M.T., Nagar, A. K. : White Blood Cell Segmentation and Watermarking. In: Proceedings of the IASTED International Symposia Imaging and Signal Processing in Healthcare and Technology, ISPHT 2011, Washington DC, USA (2011)
Feminna, S., Robinson, T., Mammen, J.J., Nagar, A.K.: Detection of plasmodium falciparum in peripheral blood smear images. In: Bansal, J.C., Singh, P., Deep, K., Pant, M., Nagar, A. (eds.) Proceedings of BICTA 2012. AISC, vol. 202, pp. 289–298. Springer, Heidelberg (2013)
Feminna, S., Robinson, T., Michael, J., Maqlin, P., Mammen, J.: Segmentation of sputum smear images for detection of tuberculosis bacilli. BMC Infectious Diseases 2012 12 (suppl. 1), O14 (2012)
Feminna, S., Robinson, T., Nagar, A.K., Mammen, J.J.: Segmentation of peripheral blood smear images using tissue-like P-Systems. IJNCR–BICTA 2011 Special Issue 3(1), 16–27 (2012)
Feminna, S., Thomas, H.M.T., Mammen, J.J.: Segmentation and reversible watermarking of peripheral blood smear images. In: Proceedings of the IEEE Conference on Bio Inspired Computing: Theories and Applications, vol. 2, pp. 1373–1376 (2010)
LaTorre, A., et al.: Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images. Expert Syst. Appl. 40(16), 6521–6530 (2013)
Nguyen, N.-T., Duong, A.-D., Vu, H.-Q.: Cell Splitting with High Degree of Overlapping in Peripheral Blood Smear. International Journal of Computer Theory and Engineering 3(3) (2011)
Prasad, A.S., Latha, K.S., Rao, S.K.: Separation and counting of blood cells using geometrical features and distance transformed watershed. International Journal of Engineering and Innovative Technology (IJEIT) 3(2) (2013)
Qi, X., et al.: Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Transactions on Biomedical Engineering 59(3), 754–765 (2012)
Sharif, J.M., et al.: Red blood cell segmentation using masking and watershed algorithm: A preliminary study. In: Proceedings of International Conference on Biomedical Engineering (ICoBE), Penang, Malaysia, pp. 258–262 (2012)
Tsai, A., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 22(2), 137 (2003)
Tulsani, H., Saxena, S., Yadav, N.: Segmentation using morphological watershed transformation for counting blood cells. International Journal of Computer Applications & Information Technology 2(3) (2013)
Vu, N., Manjunath, B.S.: Shape prior segmentation of multiple objects with graph cuts. In: Proceedings of Computer Vision and Pattern Recognition, CVPR 2008 (2008), doi:10.1109/CVPR.2008.4587450, ISBN: 978-1-4244-2242-5
Yadollahi, M., Prochazka, A.: Segmentation for object detection, http://dsp.vscht.cz/konference_matlab/MATLAB11/prispevky/129_yadollahi.pdf (retrieved November 11, 2013)
Yan, P., Shen, W., Kassim, A.A., Shah, M.: Segmentation of neighboring organs in medical image with model competition. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 270–277. Springer, Heidelberg (2005)
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Sheeba, F., Thamburaj, R., Mammen, J.J., Nagar, A.K. (2014). Splitting of Overlapping Cells in Peripheral Blood Smear Images by Concavity Analysis. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds) Combinatorial Image Analysis. IWCIA 2014. Lecture Notes in Computer Science, vol 8466. Springer, Cham. https://doi.org/10.1007/978-3-319-07148-0_21
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DOI: https://doi.org/10.1007/978-3-319-07148-0_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07147-3
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