1. 7th Annual STEMtech conference
Philadelphia, PA
6 - 9 November, 2016

2. DESIGN AND CONSTRUCTION OF A MULTIPURPOSE AUTOMATED
MICROSCOPE TESTING DEVICE 

3. Learning process: Microscope (Optical Systems)
Diode Laser (how Laser light operates)
Camera (how the image is transferred into digital data)
Computer/Image processing (how to deal with images)

5. GaAlAs Laser Diode
GaAlAs laser diode used to illuminate the sample with wavelength 0.645μm
Laser diode have the advantages of small size, low power dissipation , low cost , low noise, and long life

6. Image Formation in OLM
GaAlAs laser diode

8. Border Finding
Algorithm

9. (Microscopic images from the video Capture Card)
Source Image
(Microscopic images from the video Capture Card)
Image Enhancement
Color Segmentation
Data Reduction
Object Localization
Border Image
3-D representation of the results

10. Effect of applying the Median filter to the image 100
Image before applying the median filter
X-axis of image (Pixel)
Y-axis of the image (Pixel) 50
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160
180
Median Filter applied to the image

11. * n: is he number of rows and columns in filter matrix. 50
100
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120
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160
180 50
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2. Canny Edge Detection at n = (S.D.)/1.5 within the color map range
[(A.M.) 255].
1. Canny Edge Detection at n = (S.D.)/2 within the color map range [(A.M.) 255].
Border-Finding Algorithm for five different values
of the Adaptive Thresholding method  50
100
150
200 20 40 60 80
120
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180 50
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4. Canny Edge Detection at n = (S.D.)/2 within the color map range [(A.M) ].
3. Canny Edge Detection at n = (S.D.) within the color map range [(A.M.) 255].
* n: is he number of rows and columns in filter matrix.
S.D.: is the Standard Deviation of the sub-image shown in Figure (2b).
A.M.: is the Arithmetic mean of the same image.
5. Canny Edge Detection at n = (S.D.)/2 within the color map range [(A.M.) ].

12. Image with binary object

13. Wenty Contours of the peaks image.20 40 60 80
100
120
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Wenty Contours of the peaks image [Top view] with 4 contour levels
Wenty Contours of the peaks image [Top view] with 8 contour levels
Wenty Contours of the peaks image [Top view] with 16 contour levels
Wenty Contours of the peaks image.

14. Gray level relief of the basophilic normoblast
Without applying mask process
Gray level value
y-axis of the image
(pixel)
x-axis of the image
(pixel)
(b)
Applying mask process
Gray level value
Gray level relief of the basophilic normoblast

15. -800
-600
-400
-200
200
400
600
800 50
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x-axis of the image (pixel)
y-axis of the image (pixel)
(a)
x 10-1
(b)
Derivative magnitude
y-axis of the image
(pixel)
Gray level relief derivative, after the treatment of the previous Figure with Sobel operator without applying mask process.

16. Gray level relief derivative, after the treatment of the sample
-800
-600
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200
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800 50
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y-axis of the image (pixel)
(a)
Gray level relief derivative, after the treatment of the sample
with Sobel operator by applying mask process.
x-axis of the image (pixel)
x 10-1
(b)
Derivative magnitude

17. CASE STUDY

18. Liver cells with magnification of 150x

20. Number of Pixels
Gray Scale Level

22. * n : is the number of rows and columns in filter matrix.
1. Canny Edge Detection at n = (S.D.)/2 within the color map range [(A.M.) 255].
2. Canny Edge Detection at n = (S.D.)/1.5 within the color map range [(A.M.) 255].
5. Canny Edge Detection at n = (S.D.)2 within the color map range [(A.M.) ].
3. Canny Edge Detection at n = (S.D.) within the color map range [(A.M.) 255].
4. Canny Edge Detection at n = (S.D.)/2 within the color map range [(A.M) ].
* n : is the number of rows and columns in filter matrix.
S.D. : is the Standard Deviation of the sub-image of the previous slide
A.M. : is the Arithmetic mean of the same image.

24. X-axis of image (pixel)
Y-axis of image
e (pixel)

25. Description of the specific feature
Type of feature
Values
Description of the specific feature 1.
Area
12198
– Scalar; the actual number of pixels in the region. 2.
Centroid
[ ]
–1-by-2 vector; the x-and y-coordinates of the center of mass of the region. 3.
Euler Number 1
– Scalar; equal to the number of objects in the region minus the number of holes in those objects. 4.
Bounding Box
[ ]
–1-by-4 vector; the smallest rectangle that can contain the region. 5.
Major Axis Length
–Scalar; the length (in pixels) of the major axis of the ellipse that has the same second-moments as the region. 6.
Minor Axis Length
–Scalar; the length (in pixels) of the minor axis of the ellipse that has the same second-moments as the region. 7.
Eccentricity
0.4172
–Scalar; the eccentricity of the ellipse that has the same second-moments as the region. 8.
Orientation
–Scalar; the angle (in degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region. 9.
Convex Hull
[28x2 double]
–p-by-2 matrix; the smallest convex polygon that can contain the region. Each row of the matrix contains the x-and y- coordinates of one vertex of the polygon.
10.
Convex Image
[112x123 uint8]
–Binary image (uint8); the convex hull, with all pixels within the hull filled in (i.e., set to on).
11.
Convex Area
12623
– Scalar; the number of pixels in 'Convex Image'.
12.
Filled Area
– Scalar; the number of on pixels in Filled Image.
13.
Extrema
[8x2 double]
–8-by-2 matrix; the extremal points in the region.
14.
Equiv. Diameter:
– Scalar; the diameter of a circle with the same area as the region. Computed as sqrt(4*Area/pi).
15.
Solidity
0.9663
– Scalar; the proportion of the pixels in the convex hull that are also in the region. Computed as Area/ Convex Area. 16
Extent
0.8855
– Scalar; the proportion of the pixels in the bounding box that are also in the region. Computed as the Area divided by area of the bounding box.

26. Conclusions

27. The optical laser microscope (OLM) has the advantages of:
The OLM exhibits superior resolution and contrast
Microscopes with high amplification is preferable
The workstation also provides the following properties:
The Histogram provides the Nucleus/Cytoplasm ratio for Histopathologists
Simplicity
No need of a vacuum system or high voltages
No need for specimen charging
Being essentially non-destructive
accepts image data files in different formats as produced by OLM
displays, enhances, and processes the data
provide computer control for the location and dimension of the image

28. Thank you