1. بسم الله الرحمن الرحيم
{قَالُوا يَا مُوسَى إِمَّا أَنْ تُلْقِيَ وَإِمَّا أَنْ نَكُونَ أَوَّلَ مَنْ أَلْقَى، قَالَ بَلْ أَلْقُوا فَإِذَا حِبَالُهُمْ وَعِصِيُّهُمْ يُخَيَّلُ إِلَيْهِ مِنْ سِحْرِهِمْ أَنَّهَا تَسْعَى، فَأَوْجَسَ فِي نَفْسِهِ خِيفَةً مُوسَى، قُلْنَا لا تَخَفْ إِنَّكَ أَنْتَ الأَعْلَى، وَأَلْقِ مَا فِي يَمِينِكَ تَلْقَفْ مَا صَنَعُوا إِنَّمَا صَنَعُوا كَيْدُ سَاحِرٍ وَلا يُفْلِحُ السَّاحِرُ حَيْثُ أَتَى}. (سورة طه:65ـ69)

2. Computer based system for biophysical classification of white blood cells images
Submitted by
Sherif Abbas Mousa
Assistant of Physics Ain Shams Univ.
Supervisors
Dr Ibrahim Hassan
Assi. Prof of Biophysics Ain Shams Univ.
Dr Samy Kamal Hindawi
Assi. Prof of Computational physics Ain Shams Univ.
Prof. Dr Ali Abo-Zaid
Prof of computer Science 6’ Octobar Univ.

3. Intelligent Systems
Humans have the natural abilities to speak, to see, to think, to smell, to sense etc. Machines do not have such inborn abilities, but only have simple engines to follow logical algorithms.
The procedure to have the computer obtain the similar natural abilities like speaking and vision, are closely related to building knowledge system, with combination of simulation of the brain functions

4. Computer Vision What is computer vision?
“Making computers see and understanding”
Nice sunset!

5. Intelligence and Machines
Computers:
can perform precisely defined tasks quickly …
… without understanding/flexibility/or sense = ? =

6. Existence vs. Appearance of Intelligence
Note: intelligent reactions do not imply the actual existence of intelligence
Therefore: A.I. focuses on the question how machines can be made to appear intelligent
Example - Eight-Puzzle Game:

7. Image Understanding (1)
1st type of intelligent behavior for 8PG:
extraction of information from image data 3
image
Ah, it’s a 3!

8. Image Understanding (2)
(1) Image Processing
noise removal
edge enhancement
feature extraction …
(2) Image Analysis
understanding extracted features:
“Ah, it’s a curve!”
understanding combined features:
curve x + curve y “Ah, it’s a 3!”

9. Image enhancement
After enhancement
Before enhancement

15. Problem overview
1. “ White Blood cell differential (WBCs) count” is one of the vital diagnosis tests.
2. The hematologist identifies and classifies the WBCs under the light microscope.
3. According to the type of cells, there are five groups (Eosinophil (E) , Basophil (B), Neutrophil (N), Monocyte (M) and Lymphocyte (L).
4. The Hematologist counts 200 cells in each sample in order to calculate the percentage of each group.
5. The huge number of samples as well as the large number of WBCs identified in each sample increases the possibility of human error.
6. Wrong analysis causes wrong treatment of the patient. Therefore, an automated system, to over come this risk is required.

16. The blood There are two major components to human blood:
1. 55% plasma, which is the “fluid” part of the blood.

17. The blood There are two major components to human blood:
1. 55% plasma, which is the “fluid” part of the blood.
2.The blood “cellular elements” make up the other 45%.
2.1 Almost 94% of these are red blood corpuscles (erythrocytes)

18. The blood There are two major components to human blood:
1. 55% plasma, which is the “fluid” part of the blood.
2.The blood “cellular elements” make up the other 45%.
2.1 Almost 94% of these are red blood corpuscles (erythrocytes)
2.2 5% platelets.

19. The blood There are two major components to human blood:
1. 55% plasma, which is the “fluid” part of the blood.
2.The blood “cellular elements” make up the other 45%.
2.1 Almost 94% of these are red blood corpuscles (erythrocytes)
2.2 5% platelets.
2.3 Much less than 1% leukocytes,.

20. Cells Sizes
1. The RBCs are approximately 8 um diameter and are generally very regular in their size and biconcave shape.

21. Cells Sizes
1. The RBCs are approximately 8 um diameter and are generally very regular in their size and biconcave shape.
2. Platelets are about one third to one half as large as red blood corpuscle, about 2-4 um diameter.

22. Cells Sizes
1. The RBCs are approximately 8 um diameter and are generally very regular in their size and biconcave shape.
2. Platelets are about one third to one half as large as red blood corpuscle, about 2-4 um diameter.
3. White blood cells are often larger than the red cells, generally um diameter.

23. Percentage normally found
There are 5 major types of white blood cells (leukocytes).
Percentage normally found
Cell type
40-75%
Neutrophils %
Eosinophils
1-4%
Basophils %
Lymphocytes
5-10 %
Monocytes
Ref(10)

24. Blood Cell Maturation Ref. (10)
This diagram best illustrates stages of myeloid differentiation that are morphologically recognizable in the bone marrow aspirate. Stages between stem cells and myeloblast or pronormoblast have been omitted. For detail about these other stages that are not morphologically distinct, the students may refer to the lecture that Richard Steinman gave to them on hematopoiesis or Fig. 13-1, Kumar: Robbins and Cotran: Pathologic Basis of Disease, 7th ed. The lymphoid stages of differentiation are also not well addressed by this slide.
Ref. (10)

25. Neutrophils characteristics:
There are 5 major types of white blood cells (leukocytes).
1. Neutrophils
Neutrophils characteristics:
1- They have segmented nuclei typically with 2 to 5 lobes connected together by thin strands of chromatin
2- The cytoplasm stain light pink ('neutral stain‘)

26. Eosinophils characteristics:
There are 5 major types of white blood cells (leukocytes).
2. Eosinophils
Eosinophils characteristics:
1- Eosinophils have a bi-lobed nucleus
2- The cytoplasm stain Red (‘acidified stain’)

27. Basophils characteristics:
There are 5 major types of white blood cells (leukocytes).
3. Basophils:
Basophils characteristics:
1- have a large cytoplasmic granules
2- Hidden Nucleus
3- No presence of cytoplasm

28. Monocytes characteristics:
There are 5 major types of white blood cells (leukocytes).
4. Monocytes:
Monocytes characteristics:
1- Monocytes are the largest cell type
2- Their nuclei are not multilobular like granulocytes
3- Their nuclei are kidney shape

29. Lymphocytes characteristics:
There are 5 major types of white blood cells (leukocytes).
5. Lymphocytes
Lymphocytes characteristics:
1- nucleus deeply staining blue
2- nucleus eccentric in location
3- relatively small amount of cytoplasm
4- the smallest cell
5- nucleus nearly circle

30. Our Objective
Development of automated computer based system for the automated differential blood count.
features extracting
Preprocessing
And
Segmentation
classification
acquisition
This is not true, replacing the hematologist by robots only

31. Materials and methods 1- Peripheral blood film preparation: Wedge technique
Blood droplet
The wedge technique is a common method for preparing a good quality peripheral blood film.
2-3 mm of blood is dropped on the slide.

32. Peripheral blood film preparation: Wedge technique
The slide is placed on a flat surface. A second slide (spreader slide) is placed on the lower slide in front of the blood drop and pulled back until it touches the drop. The blood will spread by capillary attraction. Then, the spreader slide is pushed forward at a 300 angle.

33. Peripheral blood film preparation: Wedge technique
Direction of spread
A good preparation should be thick at one end and thin at the opposite end. The area of optimal thickness should be about 2 cm long and this is the main area that should be used for the morphologic evaluation.
Manual preparation is being supplanted by automated preparations.
Feathered end
is thin
Point of application
of blood droplet
Area of optimal
thickness for
examination
Area too thick

34. 2- Slide staining
The slide stain with a Lishman stain for 15 min. and then washed by water.
The Lishman stain consist of two different stain one of them is acid (stain red) and the other is base (stain blue).

35. 3- System overview
The Panasonic WV-CP220 series digital video camera With built in automatic gain control attached to light transmission microscope with 40x objective magnification and the camera direct connected to the microscope with no eye pieces and illuminated by 50 watt halogen lamp and microscopic focus system as shown

36. Normal leukocytes seen on a peripheral blood film (1):
Net.
Eos
Bas.
Eosinophil = Cytoplasm filled with coarse, orange-red, refractile granules of uniform size. Most have two lobes, but some will have 3.
Segmented neutrophil = Most mature form in neutrophil series. Nucleus is segmented or lobated (2-5 lobes normally), connected by thin filaments of chromatin. The cytoplasm is pale with lilac (specific) granules.
Neutrophil band = Normally 5-10% of leukocytes in blood. Nucleus is indented to more than half the distance from nuclear margin. The nucleus does not constrict to the point of a filament. Increased in stress and infection.
Monocyte = Nucleus usually indented or folded. Chromatin is slightly less dense than that of a neutrophil. The cytoplasm is gray to gray-blue and may contain fine, pink granules and/or vacuoles.
Lym.
Mon

37. Normal leukocytes seen on a peripheral blood film (2):
Net.
Eos
Bas.
Lym.
Mon

38. Segmentation
The most critical step in which the WBC segmented from the background (RBCs and platelets). And then further segmentation to the cell into Cytoplasm and Nucleus.

39. Previous works
Kovalev et al [1996 ]:developed an algorithm that uses thresholding on a green component to detect Nucleus and then the cytoplasm is approximated using a circle shape.
Bikhet et al [1999 ]: have reported segmentation and classification of the 5 types of WBC’s in peripheral blood using gray images of blood smears. They use an edge detection to identify the cell and the nucleus.
Katz et al [2000] : they used the green component of the image to segment the nucleus by fixed threshold value (100) on gray scale (0-256) and then manually adjusted circular shape to enclose the cell
Sinha et al. [2003 ]: WBC's segmentation carried out in two-step process carried out on the HSV-equivalent of the image, using K-Means clustering followed by EM-algorithm. which yield a segmentation accuracy 80%

40. 4- Our Segmentation method Step 1: separating Images into its Component Bands:
The captured image was split into (Red, Green and Blue) (RGB) component bands

41. Step 2: Construction of the image blue-green difference:

42. Histogram of B-G image Graythreshold Matlab function Counts
Point of segmentation
Gray level value
Graythreshold Matlab function

43. Step 3 Initial Segmentation Process:

44. Step 4: Image Improvement: A. Image Erosion:

45. B. Border clearance:

46. Step 5: Cell Mask
Selection of the maximum object area as the Cell mask

47. Smoothing WBC edge

48. Real Cell Mask

49. Cell histogram Real data Smoothed with polynomial fit data Counts
Gray level value

50. Peak identification (172,40) (112,22) (126,8) Threshold value Counts
Gray level value

51. Threshold segmentation
Pixel higher than threshold value
Pixel lower than threshold value

52. Nucleus and Cytoplasm pixels

53. Whatever Cell Location

54. For Any WBCs Type

55. Even if the cell doesn’t contain Cytoplasm

56. Even if The Cell was deformed

57. Features Extraction 1- Global Features :
These features describe the geometric properties of white blood cells They can be grouped together to produce other, more complex, shape features.
A. Nucleus area:
This feature describes the number of pixels in a nucleus.
B. Cell area:
This feature describes the number of pixels in a cell (nucleus & cytoplasm).
C. Nucleus area / Cell area:
This feature describes the ratio of nucleus area and cell area.

58. 2- Shape Features
These features describe the morphological properties of the nucleus. Since the white blood cells have different nucleus, these shape features can be the good measures for classification.
A. Compactness of the Nucleus
It is defined as
C= 4πANU / P2NU
The Compactness is a dimensionless number with a maximum value of 1 for circles. Smaller values are generated from non compact shapes.

59. B- Extracting the no. of nucleus’ lobes.
The extraction of this value has been automatically achieved with iterative erosion filtering of the binarized nucleus
Correct number of lobes corresponds to maximum number of connect objects during iterative erosion. 4 lobes are present and detected.
Extracting the number of nucleus’ lobes.

60. 1. Red, Green and blue average:
3- Color features:
1. Red, Green and blue average:
The sum of the intensity of the color divided by the number of pixels for Nucleus and Cytoplasm.
2. Difference between average Cytoplasm Blue and the average Nucleus Blue ( Crav. - Nrav.)
3. Difference between average Cytoplasm Red and the average Cytoplasm Green (Crav – Cgav).

61. Feature analysis Gaussian Distribution
Gaussian Distribution: Most important probability distribution in the statistical analysis of experimental data.
Total area under the curve is 100%
68.27% of observations lie within ± 1 std dev of mean
95% of observations lie within ± 2 std dev of mean
99% of observations lie within ± 3 std dev of mean
P(X)
Mean
std
X (feature)

62. Features Results (1)

63. Features Results (2)

64. Features Results (3)

65. Features Results (4)

66. Classification scheme
N, L, M, E or B
N, L, M, E or B
Classification scheme
Yes No
N, L, M or E
Presence of cytoplasm
Basophil
Yes
Crav - Nrav
> thr No
L, M or E
Neutrophils
Yes
Compactness
~= 1 1
No. of Nuclei
>2
3 Or >3
>3 No 2 3
Error cell
Lymphocyte
Error cell
E or M
Eosinophil
ANN
Eosinophil
Monocyte

67. Confusion matrix N L M B E Error cells N (651) 593 7 9 42 L (406) 376
Apparent Cells types N L M B E
Error cells
N (651)
60 %
593
91.1 % 7
1.1% 9
1.4 % 42
6.4 %
L (406)
30 %
376
92.6 % 30
7.4 %
M (134)
5 -10 %
111
82.8 % 23
17.2 %
B (41)
1-5 % 1
2.5% 40
97.5 %
E (21)
0.5 % 21
100 %
Real
cells
types

68. Classification results
Total no of images 1253 image.
1141 image correctly classified
Overall % correctly identified

69. Conclusion
1. This work presents a methodology to achieve a fully automated detection and classification of leucocytes by microscope color images identifying the following classes: Basophil, Eosinophil, Lymphocyte, Monocyte and Neutrophil.
2. Using of the difference between color components of the image has a significant reduction of image background complexity this improve our segmentation results.
3. Initial segmentation of the whole cell, reduces the complexity of next segmentation process to the cell Nucleus and Cytoplasm.
The feature extraction process simulates the knowledge of experts to differentiate the cells types. Those features of two types:
1. morphological features for the Nucleus (Number of Nucleus’ lobes and compactness) .
2. color features for cytoplasm (Red, Green and Blue).

70. 5. The features have a good discriminations between all types of cells (3 times stander deviation) except between the Eosinophils and the Monocytes.
6. Using of Artificial Neural Network (ANN) to differentiate between the Eosinophils and the Monocytes produce a very good results.
7. Presents results indicate that the image analysis of WBCs is achievable and it offers remarkable classification accuracy.
8. Further studies will be focused on:
1. improvement of classifications results of Monocytes (82.8%).
2. further classification of “error cells” to its proper types will shift the classification results to about 98 %.
3. identification of abnormal deformations in the cell morphology for a fully-automated diagnostic system.

71. Thank You for your attention……

72. Questions ??