An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA

Authors

  • Kamal ElDahshan Mathematics Department, Faculty of Science, AL-AZHAR University, Cairo, Egypt
  • Mohammed Youssef Electronic Engineering Department, Faculty of Engineering, AL-AZHAR University, Cairo, Egypt
  • Emad Masameer Mathematics Department, Faculty of Science, AL-AZHAR University, Cairo, Egypt
  • Mohammed A. Mustafa MIS Department, Modern Academy for Computer Science and Information Technology, Cairo,Egypt

DOI:

https://doi.org/10.14738/aivp.33.1196

Keywords:

Color segmentation, FPGA, ALL, XSG

Abstract

In the medical field, image segmentation process is considered the most essential step in image analysis. In this work, the color segmentation for acute lymphoblastic leukemia images (ALL) is applied to segment each leukemia image into two clearly defined regions: blasts and background. The ALL segmentation process is based on hue channel (H) of HSV color space as a method in segmentation of WBC from its complicated background. This work presents an efficient framework for segmentation of ALL images on a reconfigurable logic platform using Simulink, MATLAB and Xilinx System Generator (XSG). This segmentation framework is implemented on a FPGA using basic Xilinx Blockset to minimize hardware resources and lower execution time to be suitable enough for medical applications. It is designed using XSG as DSP design tool that enables the use of Simulink models, implemented in VHDL and synthesized for three different Xilinx FPGA boards.

References

(1) G. C. C. Lim,” Overview of Cancer in Malaysia”, Japanese Journal of Clinical Oncology, Department of Radiotherapy and Oncology, Hospital Kuala Lumpur, Kuala Lumpur, Malaysia,2002.

(2) J.Rawat, A.Singh, H.S.Bhadauria, I.Kumar,"Comparative analysis of segmentation algorithms for leukocyte extraction in the acute Lymphoblastic Leukemia images", Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on, vol., no., pp.245,250, 11-13,2014.

(3) P. Taylor,”Invited review: computer aids for decision-making in diagnostic radiology - a literature review”, Brit. J. Radiol.., 68:945–957,1995.

(4) C.T.N.Suzuki, J.F.Gomes, A.X.Falcao, S.H.Shimizu, J.P.Papa,"Automated diagnosis of human intestinal parasites using optical microscopy images", Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on , vol., no., pp.460,463, 7-11, 2013.

(5) V.S. Khoo, et al,“Magnetic resonance imaging (MRI): considerations and applications in radiotheraphy treatment planning”, Radiother. Oncol., 42:1–15, 1997.

(6) Q. Liao, Y. Deng,“An Accurate Segmentation Method for White Blood Cell Images”, In IEEE International Symposium on Biomedical Imaging,pp.245-248, 2002.

(7) V. Piuri, F. Scotti,“Morphology Classification of Blood Leucocytes by Microscope Images”, In IEEE International Conference on Computational Intelligence International Conference on Image, Speech and Signal Analysis, 1992, pp. 530–533, 2004.

(8) N. Venkateswaran, Y. V. Ramana Rao,“K-means Clustering Based Image Compression in Wavelet Domain”, Journal of Information Technology: 148-153, 2007.

(9) S. Mao-jun, et al.,“A New Method for Blood Cell Image Segmentation and Counting Based on PCNN and Autowave”, in ISCCSP 2008 Malta, 2008.

(10) Aimi Salihah , A.N, M.Y.Mashor , Nor Hazlyna Harun,“ Colour Image Enhancement Techniques for Acute Leukemia Blood Cell Morphological Features “, IEEE pp.3677-3682, 2010.

(11) S. Mohapatra and D. Patra,"Automated Cell Nucleus Segmentation and Acute Leukemia Detection in Blood Microscopic Images", in International Conference On Systems In Medecine and Biology , India, 2010.

(12) N. H. A. Halim, et al.,“Nucleus segmentation technique for acute leukemia”, in Proceedings of the IEEE 7th International Colloquium on Signal Processing and Its Applications (CSPA ’11), pp. 192–197, March 2011.

(13) K.A. Eldahshan, et al.,“Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis based on HSV Color Space”, International Journal of Computer Applications, 90(7): 2014.48-51, 2014.

(14) J. Rosenthal,"JPEG Image Compression Using an FPGA", MS thesis, Dec 2006.

(15) Y. Kim, K. Jun and K. Rhee.,"FPGA Implementation of Subband Image Encoder Using Discrete Wavelet Transform", IEEE TENCON, 1999.

(16) S. K.Shah, R. K.Soni and B. Shah,"FPGA Implementation of Image Compression using bottom- up approach of Quad tree technique", IETE Journal of research , Vol 57, Issue 2, Mar-Apr 2011.

(17) Yan Yaqiong; Zou Ruibin; Shi Caicheng,”JPEG2000 compression and decompression system based on Bayer image", Electronic Measurement & Instruments (ICEMI), 2013 IEEE 11th International Conference on , vol.2, no., pp.938,942, 16-19, 2013.

(18) D. Rao, S. Patil, N. Babu and V. Muthukumar,"Implementation and Evaluation of Image Processing Algorithms on Reconfigurable Architecture using C-based Hardware Descriptive Languages", International Journal of Theoretical and Applied Computer Sciences,Volume 1 Number 1, pp. 9–34, 2006.

(19) Nelson,"Implementation of Image Processing Algorithms on FPGA hardware", MS thesis, 2000.

(20) H. Taha, A. Sazish, A. Ahmad, M. Sharif and A. Amira,"Efficient FPGA Implementation of a Wireless Communication System Using Bluetooth Connectivity", IEEE, 2010.

(21) R. Mehra and S. Devi,"Efficient hardware co-simulation of down converters for wireless communication systems", International journal of VLSI design & Communication Systems ( VLSICS ), Vol.1, No.2, . June 2010.

(22) Cheolgi Kim, Mu Sun, M.Rahmaniheris, Lui Sha,"How to reliably integrate medical devices over wireless", Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2012 9th Annual IEEE Communications Society Conference on , vol., no., pp.85,87, 18-21, 2012.

(23) Z. Shanshan and W. Xiaohong,"Vehicle Image Edge Detection Algorithm Hardware Implementation on FPGA", International Conference on Computer Application and System Modeling ,ICCASM, 2010.

(24) "Xilinx System Generator User’s Guide", downloadable from;http:// www. Xilinx.com, 2010.

(25) "Xilinx System Generator User's Guide", www.Xilinx.com , www.Xilinxforum.

(26) R. Donida Labati, V. Piuri, F. Scotti,”ALL-IDB: the Acute Lymphoblastic Leukemia Image DataBase for image processing”, 2011.

(27) R. S. Ledley, M. Buas, & T. J. Colab,”Fundamentals of true-color image processing”, Proc. 10th IEEE Conf. on Pattern Recognition, Los Alamos, CA, USA, 1990.

(28) K.A. Eldahshan, et al.,“ Hardware Segmentation on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using Xilinx System Generator”, International Journal of Advanced Computer Science and Applications(IJACSA), 5(9), 2014.

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Published

2015-07-11

How to Cite

ElDahshan, K., Youssef, M., Masameer, E., & A. Mustafa, M. (2015). An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA. European Journal of Applied Sciences, 3(3), 8. https://doi.org/10.14738/aivp.33.1196