{"title":"Leukocyte Detection Using Image Stitching and Color Overlapping Windows","authors":"Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan","volume":113,"journal":"International Journal of Computer and Information Engineering","pagesStart":852,"pagesEnd":858,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10004321","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.","references":"[1]\tE. Cuevas, M. Diaz, and R. Rojas, \u201cLeukocyte Detection Through an Evolutionary Method,\u201d Complex System Modelling and Control Through Intelligent Soft Computations. Switzerland: Springer International Publishing, 2015, pp. 139-163.\r\n[2]\tG. Dong, N. Ray, and S. T. Acton, \u201cIntravital Leukocyte Detection Using the Gradient Inverse Coefficient of Variation,\u201d IEEE Trans. Medical Imaging, vol. 24, no. 7, pp. 910-924, July 2005.\r\n[3]\tM. Wang and R. Chu, \u201cA Novel White Blood Cell Detection Method Based on Boundary Support Vectors,\u201d in Proc. of the 2009 IEEE Conf. on Systems, Man, and Cybernatics, San Antonio, TX, USA, 2009, pp. 2595-2598.\r\n[4]\tJ. Wu, P. Zeng, Y. Zhou, and C. Oliver, \u201cA Novel Color Image Segmentation Method and Its Application to White Blood Cell Detection,\u201d in Proc. of the 8th Int. Conf. on Signal Processing, Beijing, China, 2006, pp. 16-20.\r\n[5]\tS. Wang, F. L. Korris, and D. Fu, \u201cApplying the Improved Fuzzy Cellular Neural Network IFCNN to White Blood Cell Detection,\u201d Neurocomputing, vol. 70, issue 7, pp. 1348-1359, April 2006.\r\n[6]\tLina, A. Chris, and B. Mulyawan, \u201cFocused Color Intersection for Leukocyte Detection and Recognition System,\u201d Int. Journal of Information and Electronics Engineering, vol. 3, no. 5, pp.498-501, September 2013.\r\n[7]\tLina, A. Chris, B. Mulyawan, and A. B. Dharmawan, \u201cA Leukocyte Detection System Using Scale Invariant Feature Transform Method,\u201d Int. Journal of Computer Theory and Engineering, vol. 8, no. 1, pp.69-73, February 2016.\r\n[8]\tV. Rankov, R. J. Locke, R. J. Edens, P. R. Barber, and B. Vojnovic, \u201cAn Algorithm for Image Stitching and Blending,\u201d \tin Proc. of SPIE vol. 5701, San Jose, CA, USA, 2005, pp. 190-199.\r\n[9]\tY. Kanazawa and K. Kanatani, \u201cImage Mosaicing by Stratified Matching,\u201d Image and Vision Computing, vol. 22, pp. 93-103, February 2004.\r\n[10]\tU. Bhosle, S. Chaudhuri, S. D. Roy, \u201cA Fast Method for Image Mosaicing Using Geometric Hashing\u201d, IETE Journal of Research, vol. 48, no. 3-4, pp.317-324, Mar 2002.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 113, 2016"}