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.
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
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.
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
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.
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
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
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
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
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.
