This document outlines various techniques for detecting brain tumors using neural networks and magnetic resonance imaging (MRI). It discusses how Hopfield neural networks, multiparameter feature blocks, Markov random field segmentation, and adaptive spatial fuzzy clustering algorithms can be used for tumor detection and segmentation. The proposed research work involves preprocessing MRI images using adaptive filters, analyzing the images through segmentation, feature extraction, and enhancement, and then using an artificial neural network for tumor detection.

1. NEURAL NETWORK BASED BRAIN TUMOR
DETECTION USING MR IMAGES
Presented by: Aisha Kalsoom
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2. OUTLINE
 Brain Tumors
 Imaging Techniques
 Artificial Neural Network in detection of Brain Tumor
 Hopfield Neural Network
 Multiparameter feature block
 Markov Random Field Segmentation
 Adaptive Spatial Fuzzy Clustering Algorithm
 Multiparameter MRI Analysis
 Active Contour Model
 Scheme of Proposed Research Work
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3. BRAIN TUMORS
 “Unstrained growth in the brain.”
 Benign Tumors
 Non cancerous Tumors
 Malignant Tumors
 Cancerous Tumors
 Primary Tumors
 Starting in brain
 Non Spreading to other parts
 Secondary Tumors
 Spreading to other parts
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4. IMAGING TECHNIQUES
 X-Ray
 Computed Tomography-CT Scan
 Positron Emission Tomography-PET
 Magneto Encephalography-MEG
 Biopsy
 Magnetic Resonance Imaging-MRI
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5. MAGNETIC RESONANCE IMAGING-MRI
 An imaging technique based on the measurement
of magnetic field vectors generated after an
excitation with strong magnetic fields and
radiofrequency pulses in the nuclei of hydrogen
atoms present in water molecules of a patient’s
tissue.
 MRI , an appropriate technique to detect the tumors
in brain automatically.
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6. HOPFIELD NEURAL NETWORK
 In 1997, Scientists presented work on
Computerized Tumor Boundary Detection using a
Hopfield Neural Network.
 A new approach for detection of brain boundaries in
medical images.
 Solution to optimization problem.
 Implementation for real time processing.
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7. AUTOMATED SEGMENTATION AND
CLASSIFICATION
 A fully automated process.
 Based on a Kohonen self organizing neural
network.
 Uses the standard T1-, T2- and PD-weighted MR
Images acquired in clinical examinations.
 Produces reliable and reproducible MR images
segmentation and classification.
 Eliminates intra and inter observer variability.
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8. MUTIPARAMETER FEATURE BLOCK
 The detection and visualization of brain tumors on
T2-weighted MR images using multiparameter
feature block.
 An analytical method to detect lesions or tumors in
digitized medical images for 3D visualization.
 Comparison of feature blocks with standardized
parameters.
 Experiments based on single and multiple slices of
the MRI dataset.
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9. MRF SEGMENTATION OF BRAIN MRI
 Markov Random Field Segmentation.
 A fully automatic 3D segmentation of Brain MRI.
 Analysis is performed on:
 The impact of noise
 Inhomogeneity
 Smoothing and structure thickness
 Segmentation algorithm captures three features:
 Nonparametric Distributions of tissues intensities
 Neighborhood correlations
 Signal inhomogeneities
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10. ADAPTIVE SPATIAL FUZZY CLUSTERING
ALGORITHM
 The input images may be corrupted by noise and
INU.
 The local spatial continuity constraint reduces the
noise effect and the classification ambiguity.
 Multiplicative bias field
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11. SEGMENTATION USING 3D FEATURE SET
 Variation brain tumor segmentation algorithm.
 Automation of manually Tumor segmentation.
 Make use of prior information about the appearance
of normal brain.
 Using manually segmented data statistical model is
obtained.
 Use of conditional model for discrimination between
normal and abnormal regions.
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12. MULTI PARAMETER MRI IMAGE ANALYSIS
 This method does not require any initialization.
 Firstly, area of tumor of single slice of MRI data set
is calculated.
 Secondly, the volume of the tumor from multiple
image MRI set is calculated.
 Provide facility and Improves followings:
 Brain tumor shape approximation
 2D visualization
 3D visualization for surgical planning
 Access to tumors
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13. SCHEME OF PROPOSED RESEARCH WORK
 Preprocessing of MRI.
 Image Acquisition
 Adaptive filters
 Image Analysis of MRI.
 Segmentation
 Feature Extraction
 Enhancement
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14. Image
To MATLAB
Adaptive Filters
Image Acquisition
Segmentation Enhancement
Results
ANN
Detection
Feature
Extraction
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15. CONCLUSION
 Different techniques and methods of ANN provide
ease and facility for the detection, classification,
segmentation and visualization of brain tumors.
ANN plays important role in the treatment of Brain
tumors.
References:
 International Journal of Computer Science and
communication Vol. 2, No. 2, July-December 2011,pp. 325-
331
 Neural Network Based Tumor Detection using MRI
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