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NEURAL NETWORK BASED BRAIN TUMOR DETECTION USING MR IMAGES

Presented by: Aisha Kalsoom

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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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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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IMAGING TECHNIQUES X-Ray Computed Tomography-CT Scan Positron Emission Tomography-PET Magneto Encephalography-MEG Biopsy Magnetic Resonance Imaging-MRI

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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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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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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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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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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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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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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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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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SCHEME OF PROPOSED RESEARCH WORK Preprocessing of MRI.

Image Acquisition Adaptive filters

Image Analysis of MRI. Segmentation Feature Extraction Enhancement

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Image

To MATLAB

Adaptive FiltersImage Acquisition

Segmentation

Enhancement

Results

ANN Detection

Feature Extraction

Software Approach

Hardware Approach

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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