Automatic system for brain tumors detection based on DICOM MRI images Surveying methodologies of from preprocessing to classifications Implementing comparative study. Proposed technique with highest accuracy and lest elapsed time.

1. Welcome our Graduation
project presentation
July 2016

2. Minia University
Faculty of Engineering
Biomedical Engineering Department
By: Basma Adham, Enas Leashaa, Fatma Sayed, Heba
Abdel-Razic,Walid Salah
Supervisor: Dr. Ashraf Mahroos

4. Outlines
 Abstract
 Motivation
 Introduction
 Methodology
 Comparative study
 Results
 Conclusion
 Future Work

5. Abstract
• Automatic system for brain tumors detection based on DICOM MRI images
• Surveying methodologies of from preprocessing to classifications
• Implementing comparative study.
• Proposed technique with highest accuracy and lest elapsed time.

6. Motivation of the project
 Prevalence of human brain tumors
 Why this Project
 Project challenges
 State of art of Automatic Brain Tumor Detection
 Proposed techniques

7. Prevalence of brain tumors
• The National Brain Tumor Foundation
(NBTF) for research in United States
estimates that 29,000 people in the U.S
are diagnosed with primary brain
tumors each year.
• Population of Egypt in 2012.... 83.9
million
• People diagnosis with cancer 108,600
• Risk of getting cancer before age 75:
15.4%
• People dying from cancer /year : 72,300

8. • Software diagnostic application expensive and not widely used in Egypt, so we
help doctors in our region to get right decision.
• This project helps medical staff in diagnosis which is the first and main part of
treatment of any disease.
• The project not widely made in Egypt so we made a start in this kind of research
in upper Egypt.
Why this Project

9. Why this Project
• Brain cancer remains one of the most incurable forms of
cancer, with an average survival period of one to two years.
• It is not easy to deal with a tumor in it like the rest of the
body's organs.
• There are more than 120 types of brain tumors.

10. • The brain is divided into regions
that control various functions.
• Damage to a region may affect
the functions it controls.
Why this Project

11.  Dataset availability was the biggest problem faced us
 Skull has the same intensity of tumor.
 large elapsed time in some techniques.
 Accuracy of classifications.
Project challenges

12. State-of-the-art of Automatic Brain Tumor
Detection
• 2006 was the first to use Digital awvelet transform (DWT
) coefficients to detect pathological brains. Projects are
developed and search progress till 2016.

13. Proposed Techniques
Watershed

14. Introduction
 Brain anatomy
 Types of brain tumors
 Proposed flow chart

15. Brain anatomy
Normal brain anatomy parts.
The brain is composed of three parts: the
brainstem, cerebellum, and cerebrum.
The cerebrum is divided into four lobes:
frontal, parietal, temporal, and occipital

16. Brain tumors
malignant
Astrocytomas
Glioblastoma.
benign
Brain tumors
malignant
primary
secondary
benign
Types of brain tumors

17. Data Acquisition
 MRI principles
 Data availability problem

18. Magnetic Resonance Imaging (MRI)
• The main diagnostic tools of brain tumor are CT and MRI.
• MRI provides a much greater contrast between the different
soft tissues of the body than (CT) does.

20. Image Representation

21. The MRI of the brain can be divided into three regions:
• white matter (WM)
• gray matter (GM)
• cerebrospinal fluid (CSF)

22. Dataset availability
• Dataset of patients in hospitals
cannot be provided without
security approval.
• The need of DICOM format
• large variety of brain tumors
• Data descriptions
Main dataset is obtained from
Safwa Radiology Center.
Second dataset from The Cancer
Imaging Archive

23. Preprocessing applied techniques
 Normalization
 Skull stripping
 Median Filter
 Gaussian Filter
 Histogram Equalization
 Edge detection
 Morphological operation
preprocessing
Skull
stripping
Median
Filter
Gaussian
Filter
Histogram
Equalization
Thresholding
Normalization
Contrast
Enhancement
Anisotropic
diffusion
Filter
Edge detection

24. Why normalization.
𝑰 =
(𝑰−𝑰_𝒎𝒊𝒏)
(𝑰𝒎𝒂𝒙−𝑰𝒎𝒊𝒏)
Datasets Normalization
Preprocessing

25. Skull stripping
Preprocessing

26. Median Filter
The median filter is a nonlinear digital filtering technique, often
used to remove salt and paper noise
Preprocessing

27. Original image Median filtered image
Preprocessing

28. Gaussian Filter
• Constant factor at front makes volume sum to 1
• Remove “high-frequency” components from the image (low-pass filter)
• Smoothing and bluring an image.
0.003 0.013 0.022 0.013 0.003
0.013 0.059 0.097 0.059 0.013
0.022 0.097 0.159 0.097 0.022
0.013 0.059 0.097 0.059 0.013
0.003 0.013 0.022 0.013 0.003
5 x 5,  = 1
Preprocessing

29. Original image Image after Gaussian filter
Preprocessing

30. Histogram Equalization
• The histogram equalization is an approach to enhance a given
image.
• Increase the intensity range of pixels
• Make smoothing image
3 2 4 5
7 7 8 2
3 1 2 3
5 4 6 6
8 5 11 13
18 18 20 5
5 1 5 8
13 11 15 18
The original image equalized image
Preprocessing

31. Histogram Equalization algorithm
1. Count the total mo. of pixels with each pixel intensity
2. Calculate probability of each pixel intensity.
3. Probability is no. of pixels divided by total no. of pixels
4. Calculate cumulative probability
5. multiply cumulative probability by constant no.
6. round the decimal no. obtained integer no.
Preprocessing

32. Brain image with tumor
Histogram Equalization
Image after Histogram equalization
Preprocessing

33. Edge detection
A location in the image where is a
sudden changing in the
intensity/color of pixels
Preprocessing

34. Edge detection
Brain image without skull Prewitt filter
Preprocessing

35. Thresholding
Original image Threshold level = o.5
Preprocessing

36. Contrast enhancement
Contrast enhancement improves the image quality by
enhancing hidden information and gives better quality.
Enhancing the contrast of images is done by transforming the
values in an intensity image, such that the histogram of the
output image approximately matches a specified histogram.
Original image before contrast
enhancement
Image after contrast
enhancement

37. Histogram before
contrast enhancement
Histogram after contrast
enhancement

38. Morphological operation
Morphological operation divides into four categories :
Erosion
Dilation
Opening
Closing

41. Segmentation techniques
 K-means clustering
 Otsu segmentation
 Region growing
 Watershed segmentation
 Fuzzy C means

42. Segmentation
Segmentation
Fuzzy C-means
Region
growing
Otsu
K-means
Watershed

43. Segmentation techniques
 K-means clustering
 Otsu segmentation
 Region growing
 Watershed segmentation
 Fuzzy C means

44. Region growing
Region growing technique groups pixels which have same
properties based on homogeneity criterion.

45. Region growing
Segmentation

46. Region growing
original image After skull removal
Segmentation

47. Region growing
Output of region growing
a b
complement of region growing
Segmentation

48. Region growing
a b
multiplied image Final image after post
processing
Segmentation

49. K-means Clustering
Flowchart of K means algorithm
Segmentation

50. K means segmentation in each iteration.
Original image Filtered image.
Segmentation

51. Segmented tumor after post processing
Segmentation

52. Otsu segmentation
Segmentation

53. Otsu segmentation
Otsu segmentation
a
c
b
Original image Skull removal Gaussian filter
Segmentation

54. Otsu segmentation
Otsu segmentation for four thresholds
The output of Otsu Final image after
normalization
Segmentation

55. Watersheld segmentation
1. Make a binary image
2. Take the nearest Euclidean distance
3. Iterate steps.
Segmentation

56. Watershed segmentation
Segmentation

57. Features Extraction
 Gray-Level Co-Occurrence Matrix (GLCM)
 Gray Level Difference Matrix
 Feature reduction using PCA
Feature Extraction

58. Gray-Level Co-Occurrence Matrix (GLCM)
GLCM Principle
Feature Extraction

59. The gray level difference method (GLDM)
The gray level difference method (GLDM). For different values of
d we calculated five texture features: energy, standard deviation,
Mean skewness and kurtosis.
Feature Extraction

60. Feature reduction using
Principle component analysis (PCA)
Summarization of data with many (p) variables by a smaller set of (k)
derived variables.
n
p
A n
k
X
Feature Reduction

61. PCA algorithm
1- Compute the mean of the data matrix.
µ =
1
𝑁
𝑖=1
𝑁
𝒳ᵢ
2- Subtract the mean from each image.
3- Compute the covariance matrix. K=WWᵀ
4- Compute the Eigen values λ and Eigen vectors e for
covariance matrix.
Solve : K e = λ e.
5-Order them by magnitude:
λ 1> λ2>… λN
The eigenvalue λ measures the variation in the direction
Feature Reduction

62. Classifications
 Neural Network
 K-nearest neighbor (KNN) classifier
 Support Vector Machine (SVM)

63. Artificial Neural Network
Biological neural network
Classification

64. Architecture of a typical artificial neural network
Classification

65. Neural network architecture
Classification

66. K-nearest neighbor (KNN) classifier
KNN
Classification

67. SVM
Classification

68. SVM types
Classification

69. GUI using region growing segmentation
Results

70. GUI using K means segmentation

72. Results of segmentation without skull removal

73. Comparative
study

76. Comparative study

77. Conclusion
• Finally; With K meand , GLCM feature extraction and back
propagation network Algorithms has been successfully tested
and achieved the best results with accuracy 96.7%. with 2.2s.

78. Future work
•Segmentation 3D tumors, But This work need available database for 3D.
•Obtaining the boundary of the tumor and plotting the safety margin in 2D
and 3D tumor. This will help doctors to make surgery based on automatic
boundary calculation. Furthermore, protect patient from further biopsy
procedure.
• Complete system with EEG to detect abnormalities with high accuracy.

79. Thank You