Brain Tumor Diagnosis Systems Based On Artificial Neural Networks And Segmentation Using MRI Safaa E. Amin and M. A.-M. Salem Faculty of Computer and Information Science, Ain Shams University in Cairo
[email protected],
[email protected]
Objective • In this research we propose and compare two approaches for brain tumor detection based on neural network and multiresolution image segmentation. • Brain tumors are then recognized and diagnosed to support the decision of neurodoctors.
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Outline • Introduction • Data Acquisition and Preprocessing
• Tumor Detection and Classification – PCA-based Brain Tumor Detection
– WMEM-based Brain Tumor Detection
• Results and Conclusion
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
Introduction • A brain tumor is a mass of unnecessary, and abnormal, cells growing in the brain. • MR imaging has an excellent soft tissue differentiation yielding a detailed images with good boundary contrast between anatomical structures.
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Introduction We investigated three types of brain tumor. Tumor Location Symptom
Acoustic
Optic glioma
Astrocytoma
Hearing Nerve
Optic nerve
Temporal Lobe
1-hearing loss 2-ringing & headache 3-weakness of face 4-balance problems
1-visual loss 2-double vision 3-rapid eye movement
1- seizure. 2-paralysis 3-problems with language
MRI
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Outline • Introduction • Data Acquisition and Preprocessing
• Tumor Detection and Classification – PCA-based Brain Tumor Detection
– WMEM-based Brain Tumor Detection
• Results and Conclusion
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
Data Acquisition & Preprocessing
MRI Scanner
Mohammed A-Megeed Salem
MRI Scan Acquired
from Scanner
Image Cutting and Normalization
Histogram Equalization
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Data Acquisition & Preprocessing
Histogram Equalization
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Outline • Introduction • Data Acquisition and Preprocessing
• Tumor Detection and Classification – PCA-based Brain Tumor Detection
– WMEM-based Brain Tumor Detection
• Results and Conclusion
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
Tumor Detection & Classification
PCA-based Feature Extraction MLP-based Tumor Classification
Preprocessing Multiresolution -based Segmentation
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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PCA-based Brain Tumor Detection
Input MRI cases
The cases code
Feature Extraction PCA
Mohammed A-Megeed Salem
Classification MLP
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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te st e rror rate
PCA-based Brain Tumor Detection 60 50 40 30 20 10 0 1
2
3
4
5
6
7
8
9
No of cases per tumor type
5
10
15
Best results were obtained with the training cases consist of 9 cases per tumor type and 10 eigen vector where all cases diagnosed correctly
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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WMEM-based Brain Tumor Detection
Segmentation
MRI Segmentation Cases
Mohammed A-Megeed Salem
Image
Binary
Extracting ROI
Image
Tumor
Classification using MLP
Type
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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WMEM-based Brain Tumor Detection Segmentation is the process of partitioning an image into meaningful non-intersecting regions or classes. White matter Edema
Csf
Tumor Grey matter
T1 MR image
Mohammed A-Megeed Salem
The segmented image
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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WMEM-based Brain Tumor Detection Initial parameter Estimate (0)
Image Vector
EM Algorithm
Iterates until converges to ML parameter Estimates
(p+1) |(p)< (p+1)| ML MAP Classifier Mohammed A-Megeed Salem
Classification matrix
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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WMEM-based Brain Tumor Detection Brain Image Haar Wavelet Transform
Haar Wavelet Transform
I1 EM Segmentation C0
EM Segmentation C1
I2 EM Segmentation C2
Classification Fusion
Segmented Image
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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WMEM-based Brain Tumor Detection
MRI
Segmented MRI
Segmentation
Mohammed A-Megeed Salem
Binary Image
Thresholding
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Tumor Detection & Classification
PCA-based Feature Extraction MLP-based Tumor Classification
Preprocessing Multiresolution -based Segmentation
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Outline • Introduction • Data Acquisition and Preprocessing
• Tumor Detection and Classification – PCA-based Brain Tumor Detection
– WMEM-based Brain Tumor Detection
• Results and Conclusion
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
Experimental Results
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Experimental Results
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Experimental Results
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Conclusions • Two approaches have been proposed for tumor detection and then classification using MLP. • The PCA-based approach extracts global features of the tumor and the scan. • The segmentation-based approach extracts local features of the tumor, i.e., location , size and shape. Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Conclusions • PCA / MLP has a peak recognition rate of 100% and average recognition rate of 78.2%. • Segmentation / MLP has a peak recognition rate of 96.7% and average recognition of 78%.
• Experimental results also show that the time taken to classify the segmented images is much less than that is taken to classify the feature vector resulting from applying PCA network.
Mohammed A-Megeed Salem
INFOS 2012 Brain Tumor Diagnosis System Based on NN and Segmentation Using MRI
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Contacts Brain Tumor Diagnosis Systems Based On Artificial Neural Networks And Segmentation Using MRI The 8th International Conference on Informatics and Systems May 14-16, 2012, Cairo, Egypt Dr. Mohammed Abdel-Megeed M. Salem Faculty of Computer and Information Sciences, Ain Shams University Abbassia, Cairo, Egypt Tel.: +2 011 1727 1050 Email:
[email protected]
