The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.

1. COMPUTING THE GROWTH RATE OF STEM
CELLS USING DIGITAL IMAGE PROCESSING
Batch Members:
M.Pratyusha
C.N.Priyadarshini
R.Sushmidha
Under the guidance of
Mrs.B.Pradeepa,M.E
Senior Assistant Professor

2. CONTENT:
 ABSTRACT
 INTRODUCTION
 SEGMENTATION
 FEATURE EXTRACTION
 PATTERN RECOGNITION
 REFERENCES

3. ABSTRACT
The influence and impact of digital image processing on modern society, science,
technology and art are tremendous and incredible. A stem cell considered to be
the undifferentiated biological unit cells is a kind of cell that can duplicate itself
over and over, providing new cells that can turn into cells with a specific
purpose. The stem cell analysis and research through the digital image
processing contributes its strong support to the doctors in the field of stem cell
research to yield the results in computerized technology rather than clinical
method. This kind of research through computerized method reduces the effects
on human body during transplantation and tends to be more time economical.
This project aims at computing the growth rate of stem cells by using
segmentation, feature extraction and pattern recognition which are the
fundamental methods of digital image processing. DRLSE algorithm is applied
for segmenting images. The DRLSE algorithm is an amalgamation of Canny
Edge Detector algorithm and DRLSE method, which uses the four well potential
function. Features are extracted from segmented images using GLCM method
and finally Support Vector Machine (SVM) is used for pattern recognition and
classification of stem cells.

4. INTRODUCTION
 STEM CELL - DEFINITION
 SOURCES OF STEM CELLS
 POSSIBLE USES OF STEM CELLS
 FLOW CHART

5. STEM CELL- DEFINITION
 Undifferentiated cells that can differentiate into specialized
cells and divide to produce more stem cells
 Through cell division-form more stem cells
Eg: Bone marrow
 Become specialized cells under critical condition
Eg: Pancreas & Heart

6. SOURCES OF STEM CELL
 Umbilical cord blood
 Bone marrow
 Peripheral blood
 Human embryos

7. ADVANTAGES OF STEM CELL
 Replaceable tissues/organs
 Repair of defective cell types
 Delivery of genetic therapies
 Delivery chemotherapeutic agents

8. FLOW CHART
INPUT IMAGE
(IMAGES FROM TIME LAPSE
VIDEO)
SEGMENTATION
(CANNY & 4 WELL DRLSE)
FEATURE EXTRACTION
(GRAY LEVEL CO
OCCURRENCE MATRIX)
PATTERN RECOGNITION
(SUPPORT VECTOR MACHINE)

9. CANNY EDGE DETECTOR
 Developed by JOHN F.CANNY in 1986
 Called optimal edge detector – satisfies three criterion:
Low Error Rate
Localized edges
Single edge response

10. CANNY-ALGORITHM
 Apply Gaussian filter to smooth the image in order to remove
the noise
 Find the intensity gradients of the image
 Apply non-maximum suppression to get rid of spurious
response to edge detection
 Apply double threshold to determine potential edges
 Track edge by hysterises
 In matlab canny is applied by using the keyword
e = edge (I,’canny’);

11. DRLSE METHOD
 Developed by C. Li, C. Xu, C. Gui, and M. D. Fox
 It uses edge-based active contour method to drive level set
function in the desired

12. FOUR WELL POTENTIAL FUNCTION
 The four well potential function is aimed to maintain the
signed distance property.
 The four well potential is used to increase the quality of
segmented image and get better accuracy.

13. EVALUATION OF SEGMENTATION
The evaluation of segmentation is carried out using the following
parameters:
 PSNR
 BDE
 PRECISION
 RECALL

14. PSNR
PSNR = 10.𝑙𝑜𝑔10
𝑁∗2552
𝑖 𝑗 𝐸 𝑖𝑗−𝑂 𝑖𝑗
2
Where N=SIZE OF IMAGE
O= ORIGINAL IMAGE
E= SEGMENTED IMAGE
MSE =
1
𝑁 𝑖 𝑗 𝐸𝑖𝑗 − 𝑂𝑖𝑗
2
where
N = SIZE OF IMAGE
O= ORIGINAL IMAGE
E= SEGMENTED IMAGE

15. PRECISION & RECALL
 PRECISION:
=
𝑡𝑝
𝑡𝑝+𝑓𝑝
 RECALL:
=
𝑡𝑝
𝑡𝑝+𝑓𝑛
Where
tp = intersection of segmented parts and ground truth
fp= segmented parts not overlapping ground truth
fn = missed parts of the ground truth

16. BOUNDARY DISPLACEMENT ERROR
The Boundary Displacement Error (BDE) measures the
average displacement error of one boundary pixels and the
closest boundary pixels in the other segmentation.
𝜇 𝐿𝐴( 𝑢, 𝑣)= u-v/L-1 for 0<u-v<L
0 for u-v<0
16

17. COMPARISON OF CASE 1 AND CASE 2
13.6
13.8
14
14.2
14.4
14.6
14.8
15
0 hours 8 hours 16 hours 24 hours 32 hours 40 hours 48 hours 56 hours
BDE
WITH CANNY
WITHOUT CANNY

18. INPUT IMAGES

19. EDGE DETECTOR OUTPUT

20. SEGMENTED IMAGE
Segmented imageSegmented image
Segmented image
Segmented image
Segmented imageSegmented image
Segmented imageSegmented image

21. EVALUATION OF SEGMENTATION

22. FEATURE EXTRACTION
 Feature extraction is a special form of dimensionality
reduction
 GLCM is a well-established statistical method for feature
extraction
Computing the co-occurrence matrix
Calculating feature based on the co-occurrence matrix

23. GLCM
 In 1973, Haralick introduced the co-occurrence matrix and
texture features
 Matrix is square with dimension Ng, where Ng is the number
of gray levels in the image
 Features used are Auto correlation, contrast, dissimilarity,
homogeneity, energy and entropy

25. PROPERTIES OF GLCM
 CONTRAST= 𝑛=0
𝑁 𝑔
𝑛2 𝑖 − 𝑗 = 𝑛𝑝 𝑑(𝑖, 𝑗)
 CORRELATION=
𝑖=1
𝑁 𝑔
𝑗=1
𝑁 𝑔
(𝑖−𝜇 𝑖)𝑝 𝑑 (𝑖,𝑗)
𝜎 𝑖,𝜎 𝑗
 HOMOGENITY =
𝑖=1
𝑁 𝑔
𝑗=1
𝑁 𝑔
𝑝 𝑑 (𝑖,𝑗)
1+|𝑖−𝑗}
 ENERGY= 𝑖=1
𝑁 𝑔
𝑗=1
𝑁 𝑔
𝑝 𝑑
2
(𝑖, 𝑗)
 ENTROPY= 𝑖=1
𝑁 𝑔
𝑗=1
𝑁 𝑔
𝑝 𝑑(𝑖, 𝑗) log 𝑝 𝑑(𝑖, 𝑗)

26. FEATURE EXTRACTION OUTPUT
FEATURES IMAG
E 1
IMAG
E 2
IMAG
E 3
IMAG
E 4
IMAG
E 5
IMAG
E 6
IMAG
E 7
IMAG
E 8
AUTO
CORRELATI
ON
1.1833 1.2014 1.218
1
1.2705 1.3191 1.3701 1.3668 1.3880
CONTRAST 2.4170 2.8029 2.638
1
2.9597 2.6180 4.0452 49899 5.5527
ENTROPY 3.3671 3.4131 3.760
6
4.3084 4.5614 5.3890 56441 5.9454
ENERGY 8.5460 8.4835 8.319
8
7.9916 7.7754 7.3479 7.2549 7.8074
DISSIMLARI
TY
2.4170 2.8029 2.638
1
2.9597 2.6180 4.0452 4.9899 5.5527
HOMOGENI
TY
9.8791 9.8958 9.868
0
9.8520 9.8690 9.7977 9.7505 9.7223

27. PATTERN RECOGNITION
 Pattern recognition is the process of classifying input data into
objects or classes based on key features
 SVMs introduced in COLT-92 by Boser, Guyon & Vapnik

28. SUPPORT VECTOR MACHINE

29. GROWTH RATE & ACCURACY
 Growth rate =
[log2(𝑁 𝑛+1− 𝑁 𝑛)]
(𝐷 𝑛+1− 𝐷 𝑛)
 Accuracy = (t/n)*100

30. GROWTH RATE & ACCURACY TABULATION
INPUT GROWTH RATE ACCURACY %
IMAGE 1 20481 33.65
IMAGE 2 36295 33.65
IMAGE 3 46419 44.44
IMAGE 4 22004 45.81
IMAGE 5 55562 53.92
IMAGE 6 55492 51.21
IMAGE 7 55688 47.31
IMAGE 8 27055 56.70

31. REFERENCES
[1] Nuseiba M.Altarawneh,Suhuai Luo,Brian Regan and
Changming Sun, ”A Modified Distance Regularized Level Set
Model for Liver Segmentation from CT Images,” Signal & Image
Processing : An International Journal(SIPIJ), vol.6,No.1,February
2015.
[2] Arathi J.Vyavahare, “Canny based DRLSE Algorithm for
Segmentation,” International Journal of Computer Applications,
vol.102-No.7,September 2014.
[3] C.Xu,d.l.pham,and j.1.prince,”Medical Image Segmentation
Using Deformable Models,” in SPIE Handbook on Medical
Imaging vol.3,J.M.Fitzpatrick and M.Sonka,Eds.,ed,2000,pp.129-
174.

32. CONTD…..
[4] Punam Thakare,”A Study of Image Segmentation and Edge
Detection Techniques,” International Journal on Computer
Science and Engineering,vol 3, no.2 89-904,2011.
[5] Chuming Li,Chenyang Xu,”Distance Regularized Level Set
Evolution and its Application to Image Segmentation”, IEEE
Trans on Image Processing,vol 19,no 12,December 2012.
[6] Jiafu Jiang , He Wei , Qi Qi ,” Medical Image Segmentation
Based on Biomimetic Pattern Recognition”, World Congress on
Software Engineering,IEEE Computer Society. Vol:2,Pp.375-
379,2009.

33. THANK YOU