FPGA implementation of image processing technique for blood samples characterization

This work presents a hardware implementation of an image processing algorithm for blood type determination. The image processing technique proposed in this paper uses the appearance of agglutination to determine blood type by detecting edges and contrast within the agglutinated sample. An FPGA implementation and parallel processing algorithms are used in conjugation with image processing techniques to make this system reliable for the characterization of large numbers of blood samples. The program was developed using Matlab software then transferred and implemented on a Vertex 6 FPGA from Xilinx employing ISE software. Hardware implementation of the proposed algorithm on FPGA demonstrates a power consumption of 770mW from a 2.5V power supply. Blood type characterization using our FPGA implementation requires only 6.6s, while a desktop computer-based algorithm with Matlab implementation on a Pentium 4 processor with a 3GHz clock takes 90s. The presented device is faster, more portable, less expensive, and consumes less power than conventional instruments. The proposed hardware solution achieved accuracy of 99.5% when tested with over 500 different blood samples.

[1]  Stanislaw Osowski,et al.  Data mining techniques for feature selection in blood cell recognition , 2006, ESANN.

[2]  M Garretta,et al.  The Groupamatic System for Routine Immunohematology , 2003, Transfusion.

[3]  K. R. Nataraj,et al.  Development of Algorithm for Demodulator for Processing Satellite Data Communication , 2009 .

[4]  Jorge A. Velez-Enriquez,et al.  Design and simulation of a three-phase generator for use with smart systems in medical microinstrumentation , 2011, 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE).

[5]  Vítor H. Carvalho,et al.  Automatic Determination of Human Blood Types using Image Processing Techniques , 2010, BIODEVICES.

[6]  William A. Coakley Handbook of Automated Analysis: Continuous Flow Technique , 1981 .

[7]  Qing Zheng,et al.  Direct neural network application for automated cell recognition , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[8]  Takashi Yokota,et al.  A scalable FPGA-based custom computing machine for a medical image processing , 2002, Proceedings. 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.

[9]  Navin D. Jambhekar Red Blood Cells Classification using Image Processing , 2011 .

[10]  Michael Spannagl,et al.  Automation and Data Processing with the Immucor Galileo® System in a University Blood Bank , 2007, Transfusion Medicine and Hemotherapy.

[11]  M.H. Zarifi,et al.  FPGA implementation of a fully digital demodulation technique for biomedical application , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[12]  Viktor K. Prasanna,et al.  Sparse Matrix-Vector multiplication on FPGAs , 2005, FPGA '05.

[13]  Yan Meng,et al.  MP core: algorithm and design techniques for efficient channel estimation in wireless applications , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[14]  Galen Wood Ewing,et al.  Analytical instrumentation handbook , 1990 .

[15]  Vítor H. Carvalho,et al.  Automatic System for Blood Type Classification using Image Processing Techniques , 2011, BIODEVICES.

[16]  P Sturgeon,et al.  Automation: its introduction to the field of blood group serology. , 2001, Immunohematology.