بسم الله الرحمن الرحيم

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

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

6. Example - Eight-Puzzle Game: 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

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

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 9 - 12 um diameter.

23. There are 5 major types of white blood cells (leukocytes). Ref(10)

24. Blood Cell Maturation Ref. (10)

25. 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. 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. 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. 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. 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 methods1- Peripheral blood film preparation: Wedge technique Blood droplet

32. Peripheral blood film preparation: Wedge technique

33. Peripheral blood film preparation: Wedge technique Direction of spread 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. 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] :theyused 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 methodStep 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 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 Threshold value (172,40) Counts (112,22) (126,8) Gray level value