Nano-particle Characterization Using a Fast Hybrid Clustering Technique for TEM Images by ijcsiseditor

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									                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                  Vol. 8, No. 9, December 2010

           Nano-particle Characterization Using a Fast Hybrid Clustering Technique for TEM Images

              M.A. Abdou                       Bayumy B.A. Youssef                                W.M. Sheta
            Researcher, IRI                        Researcher, IRI                            Researcher, IRI
        Mubarak City for Scientific       Mubarak City for Scientific Research       Mubarak City for Scientific Research
        Research and Technology             and Technology Applications                and Technology Applications
              Applications                       Alexandria, Egypt.                          Alexandria, Egypt
           Alexandria, Egypt.                         

    Abstract- This Paper introduces a new fast                   microscopy techniques. Chinthaka et al. [4] have
    Transmission Electron Microscopy (TEM)                       made a study where the nano-sized fluorapatite
    images clustering technique. Since analysis of               particles were synthesized using a precipitation
    particle sizes and shapes from two-dimensional               method and the material was characterized using
    TEM images is affected by variations in image                X-ray diffraction and transmission electron
    contrast between adjacent particles, automatic               microscopy (TEM). Yeng-Ming et al. [5] studied
    methods requires more efforts. The proposed                  the effect of hydrophobic molecules on the
    hybrid method consists of two main steps:                    morphology of aqueous solutions of amphiphilic
    automatic segmentation and nano-particles                    block copolymer, which has potential drug delivery
    counting. The segmentation procedure begins                  applications. Using cryogenic TEM observations,
    with an automatic threshold generator and                    micelles can clearly be visualized and their core
    moves towards a high efficient multiple- regions             size measured. Kurt et al. [6] demonstrated that
    segmentation technique. Results are observed,                TEM techniques are focusing on the determination
    compared with existing methods and manual                    of parameters, such as shape and size of islands. A
    counting.                                                    successful image contrast analysis in terms of
                                                                 shape and strain demands the application of image
    Keywords: TEM, Image segmentation, Threshold                 simulation techniques based on the many-beam
    generator, Nano-particle counting                            dynamical theory and on structure models refined
                                                                 by molecular dynamics or molecular static energy
                   1. INTRODUCTION                               minimization. Kenta et al. [7] developed a spherical
    (TEM) images are widely used in field of nano-               aberration corrected TEM technique that allowed
    particle characterization. These images should be            them to obtain clearer images in real space than
    processed via a clustering technique to obtain the           ever before. They applied this technique to titanium
    distribution of nano-particles on certain surface.           oxide, in which light elements such as oxygen are
    Mean diameter can be measured either manually                difficult to observe using TEM because of its small
    using a ruler or automatically through computer              cross section and electronic damage. Wang et al.
    algorithm. Counting pixels belonging to every                [8] examined the mean diameter of the PtRu
    cluster, calculation of mean diameter are of great           nanoparticles using        TEM. Jae et al. [9]
    importance. Manual methods require extremely                 investigated the catalysts by employing various
    hard work and suffer leak of accuracy. Automatic             physicochemical analyses: X-ray diffraction, TEM
    methods, if used properly, will be easier and attain         and extended X-ray absorption fine structure to
    a mass production in this field. Many researches             investigate the structural modification, and X-ray
    have been achieved concerning the TEM image                  photoelectron spectroscopy and X-ray absorption-
    analysis. Hideyuki et al. [1] have presented a study         near-edge spectroscopy to characterize the change
    of construction a 3D geometric model of dermatan             in electronic features. The data processing was
    sulfate glycosaminoglycans and collagen fibrils,             performed with XPSPEAK software program.
    and to use the model to interpret TEM                        Because of the lack of Tem image processing, the
    measurements of the spatial orientation and length           MRI and Gel image processing were considered in
    of dermatan sulfate glycosaminoglycans in the                survey. Because both MRI and Gel images are look
    medial collateral ligament of the human knee. This           like TEM images, whereas they have grey level
    study shown how a 3D geometric model can be                  color and the data exists in cluster spots within
    used to provide a priori information for                     image background. Many researches have been
    interpretation of geometric measurements from 2D             achieved in the Tem image processing (clustering
    micrographs. Schaeublin et al. [2] presented the             through image segmentation). Atkins et al. [10]
    usage of TEM image simulations for couple the                used thresholding and morphology techniques,
    results from molecular dynamics simulations to               combined with an anisotropic diffusion process to
    experimental TEM images. David et al. [3]                    localize and segment the brain. Ravinda et al. [11]
    synthesized discrete single-element semiconductor            proposed a similar approach. Hahn and Peitgen.
    nano-wires and multicomposition nano-wire                    [12] proposed a solely intensity-based watershed
    hetero-structures, and then characterized their              algorithm, which makes use of a simple merging
    structure and composition using high-resolution              criterion to avoid the over segmentation problem.
    electron microscopy and analytical electron                  Kapur et al. [12] proposed a hybrid approach that

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                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                              Vol. 8, No. 9, December 2010

used morphological operations and active contour              from an electronic microscope. In this type of
segmentation. Shattuck et al. [14] used adaptive              images, features are of great importance. Moreover,
anisotropic diffusion, edge detection and                     errors are not accepted as applications are very
morphological erosions to identify the brain                  restricted to scientific and research purposes. This
component. Xu et al. [15] introduced the                      algorithm deals with TEM grayscale images. These
deformation of the active surface under a gradient            images are acquired by electronic microscope
vector field computed from a binary edge map.                 which are used for characterize the material
Zeng et al. [15] used a coupled surface evolution             nanostructure. The proposed hybrid segmentation
to extract bounding surfaces of the cortex. Kim et.           method consists of two main steps:
al. [17] proposed a hierarchical segmentation based             (1). an automatic threshold generator (ATG)
on thresholding and the detection of watersheds.                (2). High       efficient     multiple-    regions
They first pre-processed the images to remove                         segmentation filter.
noise and enhance contrast, and then thresholding
was applied. Takahashi et. al. [18] achieved image            2.1. AUTOMATIC THRESHOLD GENERATOR
enhancement and smoothing based on the                                         (ATG)
definition of threshold values, before defining local
maxima in order to label the spots. Umer et al. [19],         The proposed method starts with an automatic filter
presented a technique that uses the clustering                threshold generation. This ATG aims to get exact
techniques like K-mean and fuzzy C-mean to                    threshold values used for electronic microscopic
distinguish between different types of protein spots          input image segmentation. To perform this task,
and unwanted artifacts. Christopher et al. [20]               the following step actions are considered:
presented a new technique using the labeling of               • An input image histogram (contrast and
each image pixel as either a spot or non-spot and                  homogeneity) is first generated for each input
used a Markov Random Field model and simulated                     electronic microscopic image data
annealing for inference. Neighboring spot labels              • Image histogram impulse and false data pixels
were then connected to form spot regions.                          removal is then applied to reduce errors and
Feature extraction is usually based on computing                   increase accuracy of threshold selection
commonly used features: mean variance,                        • The input image histogram obtained is fitted to
coefficient of correlation, contrast, homogeneity,                 a suitable probability distribution function
skew, and kurtosis [21]. Texture segmentation has                  (PDF) to detect its mean and lobe width
been improved by the use of co-occurrence                     • A histogram fingerprint for each input image is
matrices [22, 23]. As the “texture” contained in our               then defined
electronic microscopic images is not of a regularly
repeating variety, it is not clear whether these                     2.1.1. HISTOGRAM GENERATOR
features would help in segmenting the images                  The input image is preprocessed first to enhance its
manually. Moreover, the seven features used in                quality. Histogram shows the intensity distribution
[21] for segmentation when applied on electronic              within the input image. Image histogram serves in
microscopic images lead to large calculations and             observation of homogeneity or non- homogeneity
high complexity. In this paper, we will present a             in different image areas, and thus thresholds
hybrid feature extraction method based on wavelet             decision making within the next step, as shown in
transform and pixel texture parameters for                    Fig 1.
microscopic image regions. Those used parameters
are: pixel intensity, mean, and variance. The
proposed technique is described in the incoming
section.                                                      2500

            2. HYBRID TECHNIQUE                               2000
Although it may suffer from real time problems,
texture- based algorithms are essential in different
sections (segmentation, registration, denoising…              1500

etc.). As previously described, the commonly used
features in almost all texture based segmentation             1000
methods are: mean, variance, coefficient of
correlation, contrast, and homogeneity         [21].
Taking all those features leads to large
computations and thus more complexity. In this
paper, we present an automatic hybrid method for                 0
feature extraction. Based on wavelet transform,                      0        50       100         150        200         250
pixel intensity, image mean, and variance this                           Fig 1: Input image histogram shape.
proposed technique is applied to images acquired

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      2.1.2. HISTOGRAM IMPULSE DATA                              N1 and N 2 , as in “(3)”. Fig 2 shows the
                                                                 histogram fingerprint details.
An impulse data filter is used to omit random
values within the histogram data to avoid presence                       n
                                                                 if      K<    BW = k − 1
of false critical points, which in turns lead to a false                  2
threshold selection.
                                                                 If  K>       BW = n − k                             (3)
      2.1.3. HISTOGRAM FITTING METHOD                                    2
                                                                 And N1 = fingerpr int( K − BW )
Observing Fig 1 for the set of input images
acquired from the electronic microscope, it could                        N 2 = fingerpr int( K + BW )
be easily concluded that their histogram data could
be fitted to a suitable PDF curve. Two distributions             Applying this proposed threshold selection method
are selected according to the histogram of each                  on a wide data set, it has shown impressive results
input image: the Normal distribution and the                     except when the center of the main lobe (K) lies at
Poisson distribution, as shown in “(1)”,”(2)”                    either one of the ends of the histogram fingerprint.
respectively. The advantage of this fitting method               To overcome this problem, we will introduce a
is that both distributions have known mean and                   binary constant, the ‘deviation factor’ that will be
                                                                 used in both cases for better values of N1 and N 2
                                                                 . Equations (4) summarizes the effect of this
                   −( x − μ ) 2                                  constant:
        1λ           2σ 2
I =            e                                 (1)
      σ 2π
                                                                 If K = 1 or 2
      λ e −λ                                                     BW = K - 1
I=                                               (2)
        k!                                                       N1 = fingerprint(K − BW ± deviation)
The mean and bandwidth of this fitted probability                N 2 = fingerprint(K + BW )
distribution function is recorded. Furthermore, side                                                                   (4)
lobes are to be taken into consideration by critical             If  K = n or (n − 1)
points determination; critical points are those pixels           BW = n - K
where maximum or inflection points are found.
Gradient methods are applied to the fitted curve to              N1 = fingerprint(K − BW )
detect these critical points. Results of this gradient            N 2 = fingerprint(K + BW ± deviation)
method are: centers of side lobes, and inflection
points: {(I1, ∆1), (I2, ∆2), (I3, ∆3) …}; Where I
and ∆ represent the lobe center and lobe width                   This binary constant moreover add an integer factor
respectively. Since the percentage of infected                   of safety called "Deviation Factor" that could be
pixels is usually small enough, infected pixels                  varied
within the input image are usually away fro

nnn the detected lobes (especially the main lobe).                                                BW
Obviously this histogram analysis leads to a good
selection of segmentation thresholds or a successful                                               k
         2.1.4. FEATURE EXTRACTION
                                                                               Histogram Fingerprint
An exact method is used to determine threshold
values, “Histogram Fingerprint”. What is the
meaning of a histogram fingerprint? It is a simple                    Consider K as the order of the main lobe
array that contains the exact places and values of                    center, and n as the length of the fingerprint
histogram critical points (main lobe center- main
lobe width- side lobes centers- side lobes widths).
Observing these values, we can design the suitable                             Fig 2 : Histogram Fingerprint.
filter accurate parameters: center K and bandwidth

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    2.2.       THE HYBRID ATG- SEGMENTATION                             method mainly depends on getting certain relation
                           PHASE                                        between each pixel and its neighbors. The stopping
                                                                        criterion starts when the clustering results remain
A region of interest (ROI) is one area or multiple                      unchanged during further iterations. This counting
areas of an image of remarkable importance or                           yields to:
distinct features from the rest of the image. ROIs
are usually filtered for further investigations.                             (1). Start with two arrays (A, B) that both have
Several methods were proposed in this area either                                 a number of elements corresponding
for a single ROI [24] or multiple ROIs filtration                                 image pixels.
[25]. We will define existing ROIs by creating one                           (2). Fill these arrays with a large integer
‘binary mask’- a binary image that is the same size                               number, that must be greater than the
as the input image with pixels that define the ROI                                expected particles number (M)
set to 1 and all other pixels set to 0- for all regions.                     (3). Put a variable N as counter for particle
Infected regions or ROIs are selected according to                                Number
following procedure:                                                         (4). Start N=0
• Outputs of the histogram fingerprint analyzer                              (5). Calculate every array elements according
     (described in the previous section) are first                                the next relation
• A binary mask is obtained;                                                      Bi , j = Min ( Ai +1, j , Ai −1, j , Ai , j +1 , Ai , j −1 )
• Input image is filtered according to this mask
Fig 3 shows the block diagram of the whole
system. It should be noticed that input data set                             (6). If     Ai , j is equal to the larger number M
suffers a major segmentation problem; random
occurrence of regions. In the incoming section, we
                                                                                  then    N = N + 1 and Ai , j = N
will apply this proposed segmentation procedure on                           (7). If Ai , j is less than M , Ai , j remains
a wide data set and observe the results. Moreover,                                unchanged
we will verify our proposed methods to segment
electronic microscopic images with unexpected                                (8). Getting      r = A− B
number and shapes of regions of interest.
                                                                             (9). If r equals zero, the solution is
                                                                                  accomplished. Else repeat steps 5,6,7,8,9
            Input                                                                 after putting A = B
                                                                                       2.4. PARTICLE DIAMETER
                                 Impulse                                The area of particle is denoted by (S), S is
                                  Data                Histogram         calculated by multiplying the number of pixels
       Generator                                                        contained in the particle by the scale factor. If the
                                                                        particle area S is considered as circle the diameter
                                                                        (D) can be calculated according next relation

                                                    Threshold –
                                                      Based                          S
                                                   Segmentation          D=2
                                                                        Where   π      is equal 3.14
         Fig 3: The proposed hybrid ATG- Segmentation method.

            2.3.    PIXEL COUNTING METHOD                                       3.       RESULTS AND DISCUSSION
Output of the previous section is a binary image,                               3.1.      THE SAMPLE RESOURCES
where nano-particles are represented by black
pixels and the background is represented by the                         The samples of TEM are obtained from obtain from
white areas. Clustering of those black pixels is                        the literatures surveying such as Ref. [8] and Ref.
preformed taking into consideration the                                 [26] as shown in Fig 4 (a) (b) (c) (d) (e). They
connectivity between them. The connectivity will                        represent a variety of image of TEM image with
be accomplished using an iterative method. The                          different conditions.

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                      (a)                                              (b)                                                (c)

                                             (d)                                                 (e)

                                                 Fig 4 Sample Case studies chosen from [8 and 26].

            3.2. RESULTS OF THE PROPOSED                                       acquired when fitting the histogram curve to normal
            THRESHOLD- BASED SEGMENTATION                                      and Poisson PDF respectively. Observing several
                             METHOD                                            results, it can be concluded that the histogram curve
    It is applied to a wide data set. Images acquired from                     fitted to normal distribution will give more realistic
    an electronic microscope are all used. Fig 5 shows                         results. All incoming tests are performed by fitting to
    segmentation method by applying threshold values                           normal distribution

                            (a)                                              (b)                                         (c)
      Fig 5 : Segmentation method with threshold values acquired by fitting the histogram curve to normal and Poisson PDF respectively (a) TEM
      image (b) TEM image processed according to Normal distribution (c) TEM image processed according to Poisson distribution. Filter edges
                                                        (N1=80 and N2=190) grayscale level.

    Fig 6 shows the processed TEM image of Fig 4 (a)                           distribution indicates one main lobe at 148, the filter
    [8] using the proposed algorithm and the associated                        edges are at (N1=110 and N2=190) and the deviation
    particle size histogram. This case was chosen as an                        factor are equal to zero. Fig 7 shows the processed
    example of TEM images with a partially unclear                             TEM image of Fig 4 (b) [8] processed image using
    background and the particles have some variation of                        our algorithm and the associated particle size
    intensity; hence, the histogram fingerprint                                histogram. In this case, the histogram fingerprint

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distribution indicates one main lobe at 148, the filter                                   TEM images with a partially unclear background and
edges are at (N1=128 and N2=244) and the deviation                                        the particles have some variation of intensity; hence,
factor are equal to zero, 510 particles could be                                          the histogram fingerprint distribution indicates one
counted against manually counted of nearly 600                                            main lobe at 174, the filter edges are at (N1=174 and
particles. Fig 8 shows the processed TEM image of                                         N2=220) grayscale level and the deviation factor are
Fig 4 (c) [8] and the associated particle size                                            equal to zero. 70 particles could be counted against
histogram. This case was chosen as an example of                                          manually counted of 79 particles.




                                                                      Frequency %



                                                                                                 2         3      4      5       6       7   8   9        10    11     14

                                                                                                                       Diameter (nm)

                     (a)                                                                                   (b)
                           Fig 6: (a) Processed TEM image of Fig 4 (a), (b) associated particle size histogram

                                                                            Frequency %

                                                                                                       5         6           7       8       9       10        11.5
                                                                                                                          Diameter (nm)

                     (a)                                                                                   (b)
                           Fig 7: (a) Processed TEM image of Fig 4 (a) (b) associated particls size histogram.



                                                                      Frequency %




                                                                                                 0.5       1     1.5     2  2.5 3      3.5       4        5    5.5    6.5
                                                                                                                         Diameter (nm)

                           (a)                                                                                   (b)
                           Fig 8 : (a) Processed TEM image of Fig 4 (c), (b) associated particls size histogram.

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Fig 9 shows the processed TEM image of Fig 4 (d)                            fingerprint distribution indicates one main lobe at
[8] and the associated particle size histogram. This                        174, the filter edges are at (N1=114 and N2=244)
case was chosen as an example of TEM images with                            grayscale level and the deviation factor are equal to
a unclear background and the particles have some                            one. 116 particles could be counted against manually
variation of intensity; hence, the histogram                                counted of nearly 150 particles.




                                                                                    Frequency %



                                                                                                         1.5          2       2.5    3       4     4.5      5
                                                                                                                             Diameter (nm)

                            (a)                                                                                (b)
            Fig 9: (a) Processed TEM image of Fig 4 (d) using the proposed algorithm (b) Associated particls size histogram.

Fig 10 shows the processed TEM image of Fig 4 (e)                           174, the filter edges are at (N1=128 and N2=244)
[26] and the associated particle size histogram. This                       grayscale level and the deviation factor are equal to
case was chosen as an example of TEM images with                            zero. 315 particles could be counted against 369
a clear background but the particles have big                               particles counted in [26]. When the counted manually
variation of intensity; hence, the histogram                                were found 333 particles.
fingerprint distribution indicates one main lobe at


                                                                                                  1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 3.7
                                                                                                                          Diameter in (nm)

                      (a)                                                                                            (b)
            Fig 10: (a) Processed TEM image of Fig 4 (e) using the proposed algorithm (b) associated particls size histogram.

          3.3. RESULTS COMPARISON                                           particles where were obtained over 100 particles in
                                                                            randomly chosen areas not over the all image. The
The comparison has been preformed based on the                              Fig 11 shows the particle size histogram of [8]
results of Refs. [8, 26]. The case of Fig 4 (c) from                        against the particle size histogram using our
Ref. [8] and the case of Fig 4 (e) from [26] are                            algorithm. The results which included in Fig 11 (a)
chosen to perform the comparison. Ref [8] has                               are not reasonable because as we see in the figure the
proposed a manual method to count and size the nano

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summation of the frequency percentage over the all                       registered particle size is nearly equal to 135%.

                      (a)                                                                         (b)
                              Fig 11: Particle size histogram (a) of Ref. [8] (b) The proposed algorithm.

Ref. [26] has proposed that the contrast and darkness                    counted in [26] and 333 particles manually counted.
of the separate sections of the TEM image were                           The results which included in Fig 12 gives an
adjusted in Adobe Photoshop prior to the analyzing                       indication that the particles size has almost the same
of particles by NIH-Image. The Fig 12 shows the                          distribution trend. Fig 12 (a) shows that the standard
particle size histogram of Ref. [26] against the                         deviation and the mean diameter are 0.437 nm and
particle size histogram using our algorithm. 315                         2.4 nm respectively while Fig 12 (a) shows 0.54 nm
particles could be counted against 369 particles                         of standard deviation and 2.3 nm of mean diameter.


                                                                                           1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 3.7
                                                                                                             Diameter in (nm)

                            (a)                                                                              (b)
                              Fig 12: Particle size histogram (a) Ref. [26] (b) The proposed algorithm.

                4.   CONCLUSION                                          and proposed techniques shows the impact of our
The presented method for nano-particles size
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                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                             Vol. 8, No. 9, December 2010

        Proceedings of SPIE Conference on Medical                             ____________________________________________________________________ 
        Imaging, 2009.                                                                                     
[21].   A. Solberg and A. Jain, “Texture fusion and                                                    Dr. Bayumy A. B. Youssef is a
                                                                                                       researcher in computer graphics
        feature selection applied to SAR imagery,”
                                                                                                       department, Information technology
        IEEE Transactions on Geoscience and Remote
                                                                                                       institute, Mubarak City for Scientific
        Sensing, vol. 35, no. 2, pp. 475–479, 3 1997.                                                  Research         and       Technological
[22].   D. Geman, C. Geman, C. Graffigne, and D.                                                       Applications, Alexandria, Egypt. He
        Pong, “Boundary detection by constrained                                                       received Bs.c. and Ms.c. degrees from
        optimization,” IEEE Trans. Pattern Anal.                                                       Mechanical Engineering department,
        Mach. Intell., vol. 12, no. 7, pp. 609–628, Jul                                                faculty of Engineering, Cairo University,
        1990.                                                                 Egypt. He received his Ph.D degree from the Information
                                                                              Technology department, institute of graduate studies and research,
[23].   R.M. Haralick, “Statistical and structural
                                                                              Alexandria University 2004. His research interests include
        approaches to texture,” Proc. IEEE, vol. 67,                          Numerical modeling and Simulation, Visualization of Scientific
        no. 5, pp. 786–804, May 1979.                                         Data, Mesh generation product, Investigating Conservation and
[24].   M. Abdou, M. Tayel, “Automated Biomedical                             Restoration of Culture Heritage and (2D & 3D) Image Processing
        System for Image Segmentation and                                     (Segmentation, Restoration, &registration)
        Transmission,” The International Journal of
        Robotics and Automation, Vol. 23, Issue 1,                            __________________________________________________
                                                                                                        Walaa M. Sheta is an associate
[25].   M. Abdou, “An Introduced Blocked-Wavelet                                                        professor of Computer graphics in
        Algorithm for Signal and Time Improvements                                                      Informatics Research Institute at
        of Images with Multiple Regions,” CGIM                                                          Mubarak city for Scientific Research
                                                                                                        (MUCSAT) since 2006. During 2001-
        2008, 10th IASTED International Conference                                                      2006 he has worked as Assistant
        on Computer Graphics and Imaging,                                                               professor at MUCSAT. He holds a
        Innsbruck, Austria, pp.34- 39, Feb. 2008.                                                       visiting professor position at University
[26].   H. Woehrle, E. Hutchison, S.Ozkar2, G. Finke                                                    of Louisville in US and University of
                                                                                                        Salford     in    UK.      He     advised
        “Analysis of Nanoparticle Transmission                                                          approximately 20 master’s and doctoral
        Electron Microscopy Data Using a Public-                                                        graduates, his research contributions and
        Domain Image-Processing Program, Image” ©                             consulting spans the areas of real-time computer graphics, Human
        Tubitak ,Turk J Chem vol. (30) P.p. 1-                                computer Interaction, Distributed Virtual Environment and 3D
                                                                              image processing. He participated and led many national and
        13,(2006).                                                            multinational research funded projects. He received M.Sc. and PhD
                                                                              in Information Technology from University of Alexandria, in 1993
             AUTHORS PROFILE                                                  and 2000, respectively. He received B.Sc. from Faculty of Science,
_________________________________________                                     University of Alexandria in 1989

                             Dr.  Mohamed  Abd­ElRahman 
                             Abdou  is  an  assistant  professor  in 
                             Communications  Engineering.  He 
                             obtained  his  PhD  in  2006  when  he 
                             started  a  tenure  track  as  a 
                             researcher  at  Mubarak  City  for 
                             Sciences  &  Technology.  In  2008  he 
                             joined  Pharos  University  in 
                             Alexandria  as  an  assistant 
                             professor in the EE department. His 
                             research interests are in the area of 
                             image  and  signal  processing, 
                             biomedical       engineering,     and 
knowledge  based  systems  (fuzzy  control‐  Neural  Network‐ 
cellular automata). He published his first international book in 
February 2010 in medical image processing.  His is  a reviewer 
of several international journals: ELSEVIER, IASTED, WSEAS 

                                                                                                               ISSN 1947-5500

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