Automatic Multimodality Brain Tumor Detection

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 3, March 2013)

264

Automatic Multimodality Brain Tumor Detection

Kavitha.C

1

, S.Sangeetha

2

1_{PG Scholar, Sri Shakthi Institute of Engineering and Technology, Chinniampalayam, Coimbatore-641062} 2

Assistant professor/ECE, Sri Shakthi Institute of Engineering and Technology, Chinniampalayam, Coimbatore-641062

Abstract - Automatic detection of brain tumor is a difficult task due to variations in type, size, location and shape of tumor. A multi-modality framework for automatic tumor detection by fusing different Magnetic Resonance Imaging modalities including T1-weighted, T2-weighted, and T1 with gadolinium contrast agent. The intensity, shape deformation were extracted from each image. The Multi-modal MR images with simulated tumor have been used as the ground truth for training using neural networks and validation of the detection method. Preprocessing is done to coordinate the number of axis. The features are extracted and it is compared and used for further processing. Segmentation describes separation of suspicious region from background MRI image. The neural network is used for training the network for classification of tumor cells. The neural network is trained with the selected feature and tumor affected regions can be detected.

Keywords- MRI, T1-WEIGHTED, T2-WEIGHTED, GADOLINIUM, MULTIMODAL

I. INTRODUCTION

The dawn of medical imaging modalities such as X-ray ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) has greatly improved the diagnosis of human diseases as they provide an effective means for noninvasively mapping the anatomy of a subject. Until recently, the most common procedure to analyze imaging data was visual inspection on printed support. Among these for Brain Imaging the MRI (Magnetic Resonance Imaging) is a most promising tool due to its soft tissue contrast and non-invasiveness.

MRI uses radio waves and a strong magnetic field rather than X-rays to provide remarkably clear and detailed picture of internal organs and tissues clusters. Medical image analysis is an important biomedical application which is highly computational in nature and requires the aid of the automated system. These image analysis techniques are often used to detect the abnormalities in the human bodies through scan images. Automated brain disorder diagnosis with MR images is

one of the specific medical image analysis

methodologies. The image analysis techniques include image preprocessing, image segmentation, histogram equalization, feature extraction, etc.

Image Preprocessing is required as the MR images consist of unwanted artifacts these are due to the operator handling MRI Machine, patient motion during imaging, thermal noise and exist of any metal things in imaging environment.

By preprocessing these unwanted artifacts are removed from the images and prepare the images for further processing like feature extraction, classification, etc. Filtering is the basic tool in image preprocessing used to remove these unwanted artifacts there are different types of filter like Gaussian, Weiner, Unsharp, etc. are used in most of the literature.

Histogram Equalization is another preprocessing tool used for equalizing the intensities in the image. This preprocessing technique is required as in the detection of edges of tumor the tumor appears very dark on the image which is very confusing. Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with the medical images where pre-surgery and post-surgery decisions are required for the purpose of

initiating and speeding up recovery purpose.

Segmentation extracts the abnormal portion from the image which is useful for analyzing the size and shape of abnormal image.

The features provide the characteristic of input to the classifiers by considering the description of the relevant properties on the image into feature space. There are different types of features like statistical features, non-statistical features, textural features, etc. In these method different types of feature extraction techniques for different types of features has been studied. The next step in the diagnostic system is Classification and followed by Segmentation. The feature vector is supplied to the classifier for classifying the Brain MRI Images into two categories namely normal brain and abnormal brain. There are several classifiers available for classifying the brain MRI images.

II. OVERVIEW OF EXISTING PROTOCOLS

A. MRI Segmentation through Wavelets And Fuzzy C-Means

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It gives better visualization of soft tissues of human body. In this paper, we propose a hybrid technique that makes use of wavelets and FCM. Through wavelets we extract the high pass image and to further enhance edge details we apply Kirch’s edge detection mask, which provides fine edge details. The noise-robust nature of wavelets and the noise-sensitivity of FCM combination

B. Threshold Based Tumor Detection

The literature is replete with the techniques that have been proposed for MR images in order to segment tumors in human brain. These include thresholding, region growing, statistical models, active contour models and

clustering. This proposes a knowledge guided

thresholding technique for brain tumor segmentation. The problem with thresholding techniques is that it is normally difficult to determine any threshold value for tumor segmentation because intensities in MRI images are normally scattered; wrong threshold selection can either neglect tumor portion or label many healthy parts as tumors. These kinds of techniques are thus not that reliable.

Region Growing techniques are also very common for tumor detection. These techniques require a seed point for each region to segment and thereby determine some suitable threshold for homogeneity. The watershed algorithm is a gradient based technique and it relay on image contrast which can be degraded during image acquisition and yields to inaccurate results. A multi-label image segmentation method for medical applications based on graph cuts was proposed. This technique is based on region adjacency formed by applying on watershed transform. Combination of morphology and graph cuts provides segmentation in a speedy way. These approaches are not attractive for medical images as these could not handle in-homogeneity in images.

C. Tumor Segmentation From A Multispectral MRI Images By Using Support Vector Machine Classification

The goal of this paper is to present a supervised system aimed at tracking the tumor volume during a therapeutic treatment from multispectral MRI volumes. Four types of MRI are used in our study: T1, T2, proton density (PD) and fluid attenuated inversion recovery (FLAIR).

For decreasing the processing time, the proposed method employs a multi-scale scheme to identify firstly the abnormal field and extract then the tumor region.

Both steps use support vector machines (SVMs). The training is carried out only on the first MRI examination (at the beginning of the treatment).

The tracking process at the time point t takes the tumor region obtained in the examination at t-1 as its initialization. Only the second step is performed for others examinations to extract the tumor region. The results obtained show that this system achieves promising results in time consumption.

D. MRI Image Segmentation Using Unsupervised Clustering Techniques

In medical image visualization and analysis, segmentation is an indispensable step in the processing of images. MR has become a particularly useful medical diagnostic tool for cases involving soft tissues, such as in brain imaging. The method develops an effective algorithm for the segmentation of the MRI images. This method have the use and implementation of fuzzy C means clustering and genetic algorithm (GA) for an automatic segmentation of white matter (WM), gray matter (GM), cerebro spinal fluid (CSF), the extra cranial regions and the presence of tumor regions. The results were analyzed and compared with the reference "gold standard" obtained from radiologists.

E. Brain Tumor Classification Using Neural Network Based Methods

MRI (Magnetic resonance Imaging) brain tumor images Classification is a difficult task due to the variance and complexity of tumors. This method presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related with MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features. In the classification stage, two classifiers based on supervised machine learning have been developed. The first classifier based on feed forward artificial neural network (FF-ANN) and the second classifier based on Back-Propagation Neural Network. The classifiers have been used to classify subjects as normal or abnormal MRI brain images.

III. PROPOSED ALGORITHM

A. Preprocessing

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B. Image Registration

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, from different times, or from different viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements. One of the images is referred to as the reference or source and the second image is referred to as the target or sensed. Image registration involves spatially transforming the target image to align with the reference image.

C. Affine Registration

Affine registration method registers each of these structures separately and interpolates the local rigid transformations based on a modified distance weighting interpolation. Based on a similar idea a piece-wise affine registration algorithm was proposed where sub-images can be automatically extracted prior to individual local registrations. The challenge of these methods is that the priors for extracting the sub-images or structures from both of the target and source images are not always available. Also, the fusion of the local rigid or affine transformations based on the final results does not guarantee a diffeomorphic global deformation field.

A poly-affine/locally affine registration frame work based on a block-matching algorithm using correlation coefficient similarity measure has been proposed. An interpolation scheme based on fusing the speed of the local transformations is employed to generate a global transformation (poly-affine) from this local set. This

algorithm also implicitly provides the inverse

transformation and guarantees a diffeomorphism.

D. Non Rigid Registration

Non rigid registration is the general term for an algorithm for the alignment of data sets that are mismatched in a nonlinear or no uniform manner. The term ''matching'' is used to refer to any process that determines correspondences between data sets.

The following series of steps are used:

1. Extract points with high local structure in one data set.

2. For each extracted point find the best

corresponding point in the second data set. 3. Check the displacement for the selected points

and remove overlapping displacements. 4. Interpolate the displacement for the selected and

matched points to get a displacement field for the whole data set.

5. Apply the interpolated displacement field on the second data set.

6. Eventually improve the alignment by using

multiple resolutions or looping.

The overall process can be improved in two ways; looping and matching using multiple resolutions. Looping is simple since the second loop does not need to know anything about the previous processing and can be seen as a completely independent matching process. If the overall displacement field should be known, i.e. not only the final match is of interest, the problem of combining the displacement fields is the same as for multiple resolution matching.

This form of multi-scale matching can certainly be improved. E.g the displacement in higher levels would not need to be applied. Combining two displacement fields though is not a trivial problem. For at least one of the transformations the inverse has to be known. The size of the search-window is adapted to the current scale.

For the lowest scale it is the specified search-window, divided by the number of scales times the scale-factor. When moving towards higher levels the search-window is a bit larger than twice the scale-factor, since any larger displacement should have been found in the lower levels.

E. Histogram Equalization

This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values.

Feature Extraction

Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. When performing analysis of complex data one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computation power or a classification algorithm which over fits the training sample and generalizes poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. In image processing, feature extraction is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation set of features.

In this proposed method we extract the following features.

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 Intensity features - Mean, Variance, Standard

Variance, Median Intensity, Skewness,and Kurtosis

 Texture features- Contrast, Correlation, Entropy, Energy, Homogeneity, cluster shade, sum of square variance.

Non Statistical Features

The non statistical features are estimated using pixel properties and other physical of the image. The intensity of the picture can be taken as the important non statistical property of the digital image. This represents the grey level of the image.

Intensity

Intensity is the most straightforward feature that has been widely used for tumor segmentation in MRI. However, it is not sufficient for robust and accurate tumor detection due to tumor intensity variation and overlap with the intensity of normal tissues.

Shape Deformation

Tumor growth results in brain anatomical deformation in the structures surrounding the tumor. This anatomical deformation is used as a feature for tumor detection. MRIs are registered to the symmetric template using a 3D non-rigid Demons registration algorithm. The magnitude of the deformation vector field resulted from the registration is calculated and normalized to form the shape deformation feature.

Texture

Texture is an important characteristic of the image, describing the spatial pattern of the neighboring pixels’ intensities, and is commonly used in image segmentation. A multi-channel filtering approach using a bank of 3D Gabor filters with different orientations and spatial frequencies is employed to extract texture feature vectors from each MRI.

Statistical Property Entropy

E=entropy(I) returns E, a scalar value representing the entropy of grayscale image I. Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image. The entropy uses two bins for logical arrays and 256 bins for uint8, uint16, or double arrays. It can be a multidimensional image. If it has more than two dimensions, the entropy function treats it as a multidimensional grayscale image and not as an RGB image.

Energy

Energy is used to describe a measure of "information" when formulating an operation under a probability framework such as MAP (maximum a priori) estimation in conjunction with Markov Random Fields.

Sometimes the energy can be a negative measure to be minimized and sometimes it is a positive measure to be maximized.

Standard Deviation

A measure of how spread out data values is around the mean, defined as the square root of the variance. Statistical index of the degree of deviation from central tendency, namely, of the variability within a distribution, the square root of the average of the squared deviation from the mean. A measure of dispersion or variation used to describe a characteristic of a frequency distribution.

Covariance

Covariance is a measure of the correlation strength of different sets of random variables (that is, the application of variance to the case of two variables. This calculation takes the product of the differences between each X and Y

pairing and the X and Y mean and then average these products. Thus, if both X and Y are above or below the mean, the product will be positive. If they vary in opposite directions, the product will be negative. A positive covariance indicates above or below average XY

pairings; negative covariance indicates pairings with one variable above and one variable below average.

F. Comparison of features

The following features are calculated and they are used to train the training circuit. The following features are only compared with the new input image with tumor. The statistical and main features are categorized and they are used in various combinations to obtain the efficient results by training process.

G. Neural network training

The classifier is designed to train the network which detects the tumour and they can be used to place the tumor cells.the tumor cells are located and they are used for automatic tumor detection.

IV. SIMULATION RESULT AND ANALYSIS

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Fig 1 image registration

Fig 2 Image Normalization

Fig 3 Features extracted

Fig 4 Tumor detection

V. CONCLUSION

The features are extracted and they are used for the tumor detection. We have developed an automatic tumor detection algorithm using multi-modal MRI. The preliminary results show 100% detection rate in all of our test sets including simulated and patient data with an average accuracy of about 90%. Our results also show that the shape deformation feature can improve the segmentation accuracy.Currently, we are validating this method on multiple healthy and pathological patient data with variable tumor characteristics. These segmented real data will be included in the training data set in order to improve the classification performance. We will employ our registration technique and tumor detection algorithm to detect tumor changes which is essential for monitoring tumor growth/shrinkage, response totreatment over time, and evaluation of surgical outcome.

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