Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30135
Leukocyte Detection Using Image Stitching and Color Overlapping Windows

Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

Abstract:

Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: Color overlapping windows, image stitching, leukocyte detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124087

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1105

References:


[1] E. Cuevas, M. Diaz, and R. Rojas, “Leukocyte Detection Through an Evolutionary Method,” Complex System Modelling and Control Through Intelligent Soft Computations. Switzerland: Springer International Publishing, 2015, pp. 139-163.
[2] G. Dong, N. Ray, and S. T. Acton, “Intravital Leukocyte Detection Using the Gradient Inverse Coefficient of Variation,” IEEE Trans. Medical Imaging, vol. 24, no. 7, pp. 910-924, July 2005.
[3] M. Wang and R. Chu, “A Novel White Blood Cell Detection Method Based on Boundary Support Vectors,” in Proc. of the 2009 IEEE Conf. on Systems, Man, and Cybernatics, San Antonio, TX, USA, 2009, pp. 2595-2598.
[4] J. Wu, P. Zeng, Y. Zhou, and C. Oliver, “A Novel Color Image Segmentation Method and Its Application to White Blood Cell Detection,” in Proc. of the 8th Int. Conf. on Signal Processing, Beijing, China, 2006, pp. 16-20.
[5] S. Wang, F. L. Korris, and D. Fu, “Applying the Improved Fuzzy Cellular Neural Network IFCNN to White Blood Cell Detection,” Neurocomputing, vol. 70, issue 7, pp. 1348-1359, April 2006.
[6] Lina, A. Chris, and B. Mulyawan, “Focused Color Intersection for Leukocyte Detection and Recognition System,” Int. Journal of Information and Electronics Engineering, vol. 3, no. 5, pp.498-501, September 2013.
[7] Lina, A. Chris, B. Mulyawan, and A. B. Dharmawan, “A Leukocyte Detection System Using Scale Invariant Feature Transform Method,” Int. Journal of Computer Theory and Engineering, vol. 8, no. 1, pp.69-73, February 2016.
[8] V. Rankov, R. J. Locke, R. J. Edens, P. R. Barber, and B. Vojnovic, “An Algorithm for Image Stitching and Blending,” in Proc. of SPIE vol. 5701, San Jose, CA, USA, 2005, pp. 190-199.
[9] Y. Kanazawa and K. Kanatani, “Image Mosaicing by Stratified Matching,” Image and Vision Computing, vol. 22, pp. 93-103, February 2004.
[10] U. Bhosle, S. Chaudhuri, S. D. Roy, “A Fast Method for Image Mosaicing Using Geometric Hashing”, IETE Journal of Research, vol. 48, no. 3-4, pp.317-324, Mar 2002.