1. Detection and Classification of Tumor in Magnetic Resonance (MRI) Brain Images
Presented by: Anum Masood
PhD Scholar
SEIEE Department, Shanghai Jiao Tong University

2. Introduction
Brain is tightly safeguarded inside skull. It works as an autonomous system that controls the entire function of our body.
It retains its autonomy, yet coordinates with rest of the body to synchronize and harmonize the overall functionality.
However, brain is not devoid of malfunctioning and disease. What makes the brain disorders unique and somewhat complicated is the fact that brain is tightly enclosed inside the skull making it difficult to approach, study and diagnose any disorder nurturing therein.

3. Introduction

4. Introduction
Recent advances in medical science have provided means for diagnostic and therapeutic ability.
Major diagnostic modalities include Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) – particularly advantageous in the identification of Brain tumor.

5. Introduction
MRI (Magnetic Resonance Imaging)

6. Introduction
CT (Computed Tomography) Scan

7. Introduction
Brain tumor is an abnormal growth of cells that changes the normal structure and behavior of brain.
It can affect the central spinal canal or cranium. Its incidence is increasing tremendously throughout the world affecting both older as well as younger population.
A brain tumor has a tendency to destroy all or most of the normal brain cells. Tumor occupies most of the brain part resulting in inflammation, swelling and increased pressure within the skull.

8. Introduction (a)(b)(c) Tumor Images
(d)(e)(f) Isolated Tumor after Segmentation

9. Detection of Brain Tumor
Automatic detection in MRI should comprise various phases i.e. preprocessing, feature extraction and segmentation/classification.
Preprocessing is used to clean up the irrelevant data and to normalize them in order to facilitate the feature extraction.
Feature extraction is performed as a next step after image reprocessing. In feature extraction technique, specific features from preprocessed image are extracted in such a way that the maximum similarity can be achieved within the class so as to minimize the intra class similarity.

10. Detection of Brain Tumor

11. Detection of Brain Tumor
Intensity based features, shape based features and texture features for MRI images can be extracted and used in classifiers for further classification.
For all classifiers, the classification accuracy and sensitivity with textural features is significantly more in comparison to both wavelet-based feature extraction techniques suggested in literature for MRI images of brain.
Image classification is considered to be the important step in automated diagnosis system to separate the normal image from the defective one.
Most popular methods used for image classification are Rule-based Methods, Decision Tree based Methods, Neural Networks based methods, Support Vector Machines, Naive Bayes and Bayesian Belief Networks.

12. Detection of Brain Tumor
Support vector machine (SVM) is a new machine learning method based on statistical learning theory. SVM is considering being the appropriate classifier for tumor detection problem because it can handle nonlinear data and achieve classification on minimum amount of data available for training
Another robust, more effective method for tumor detection based on Machine Learning (ML). ML method showed 100% sensitivity with an accuracy of 99% and increased specificity.
Bayesian formulation could be used to detect the brain tumor. Gaussian Bayesian classifier can be used for locating tumor in brain and then fluid vector flow (FVF) algorithm may also be used for estimating the shape of the tumor.

13. Proposed Work
Improved classification of normal and abnormal areas with high accuracy is a challenging task in automated tumor detection and is the major focus of this research study.
Many computer aided diagnosis techniques have already been developed.
These techniques can help the physicians to non- invasively detect the tumor but still different artifacts (Motion artifacts and ring artifacts, etc), intensity inhomogeneity or bias-field noise, partial volume effect, local noise due to electronic devices affect the accuracy of tumor detection and there is room for improvement which will be the main focus of this work.

14. Related Work
Histogram/ intensity analysis along with fuzzy rules are used for tumor segmentation and detection based on experts knowledge.
Level set along with probability model is used for tumor detection in brain MR images.
(CA) cellular automata based method for seeded tumor segmentation of CE-T1 Weighted MR images.
Level sets are used for total tumor segmentation, this method showed less sensitivity to seed initialization and useful for exact shortest path solution.

15. Related Work
Proposed tumor-cut method is computationally more efficient, show less coefficient of variance and robustness for heterogeneous tumor types and give 80%-90% overlap performance with minimal user interaction as compared to other graph-cut and grow-cut methods.
2D image segmentation based on watershed segmentation is used for shape approximation of the tumor.
Automatic segmentation and detection of tumor in MRI brain using watershed.

16. Proposed Work
Automated detection and classification of brain tumors from MRI comprises several common steps, including pre-processing, feature extraction and finally classification of data.
The main aim of preprocessing is to clean up the data and to normalize them in order to facilitate the feature extraction. These features, once selected and extracted, are used in a classifier, whose output is a brain tumor class.
Main problem lies in the segmentation of medical images because the segmentation techniques are specific to the type of body parts to be studied and imaging modalities.
However each imaging system has its own limitations which make the object recognition task even more difficult .

17. Proposed Work
MRI images suffers from the following problems which is going to be focused in this research study:
Different artifacts (Motion artifacts and ring artifacts, etc).
Intensity inhomogeneity or bias field noise
Partial volume effect
Local noise due to electronic devices
The challenge is to reduce the number of false positives to help the radiologists in correct diagnosing of abnormal areas with minimum time and to classify the brain images as normal and abnormal with greatest classification accuracy using novel segmentation and classification techniques
So, the main purpose is to build up a computerized diagnosis method for tumor isolation/detection and classification of MR brain images.

18. Thank You