2015 International Conference on Communication, Control and Intelligent Systems (CCIS)
Hybrid Approach for Brain Tumor Detection and Classification in Magnetic Resonance Images Praveen G.B. Department of Electrical, Electronics & Instrwnentation, BITS Pilani-K.K. Birla Goa Campus Goa, India E-mail Id:
[email protected] Abstract-Computerized
methods
are
used
in
medical
imaging to image the inner portions of the human body for medical diagnosis. Image segmentation plays an important role in diagnosis, surgical planning, navigation and various medical evaluations. Manual, semi-automatic and automatic methods are existing for segmentation of the region of interest. In this paper, a hybrid approach for brain tumor detection and classification through magnetic resonance images has been proposed. First phase of the proposed approach deals with image preprocessing which includes noise filtering, skull detection, etc. The second phase deals with feature extraction of MR brain images using gray
level
co-occurrence
matrix.
Third
phase
deals
with
classification of inputs into normal or abnormal using Least Squares
Support
Vector
Machine
classifier
with
Multilayer
perceptron kernel. Final phase is the segmentation of the tumor part from the brain using fast bounding box. The experiments
were carried out on 100 images consisting of 25 normal and 75 abnormal from a real human brain and synthetic MRI dataset. The classification accuracy on both training and test images was
found to be 96.63%.
Keywords-Brain
Tumor;
Medical
Imaging;
Magnetic
Resonance Images; Feature Extraction; Classification
I.
INTRODUCTION
Mass of unwanted cell development in the brain or central spine canal leads to brain twnor. Primary brain tumors and metastatic brain tumors form the two basic kinds of twnors. Primary brain tumors start and stay in the brain itself whereas metastatic brain tumors begin as cancer in different parts of the body and then spread to the brain. Age is not a factor in brain tumors, generally it is more common in older people. Approximately 70,000 new cases of primary brain tumors around the world have been diagnosed in 2014. More than 4,600 children between the ages of 0-19 have been diagnosed with brain twnor in 2014. Brain tumors constitutes for the second leading cause of cancer-related deaths in children under age 20 and in males aged 20 - 39 [1]. Cancerous brain tumors are the second most common type of childhood cancer after leukemia. Interpretation of brain tumor patients is carried out by imaging modalities. Two most significant and frequently used imaging modalities are Computed tomography (CT) and Magnetic Resonance Imaging (MRI). Calcification, hemorrhage and assessment of bone changes related to a tumor are more predominant in CT, however MRI is the modality of decision 978-1-4673-7541-2/15/$31.00
it 2015 IEEE
Anita Agrawal Department of Electrical, Electronics & Instrumentation, BITS Pilani-K.K. Birla Goa Campus Goa, India E-mail Id:
[email protected] for assessing patients with symptoms representing a brain tumor. The methodology of brain tumor characterization requires a rather intricate evaluation of the various MR images and spectra features and is typically performed by experienced radiologists [2]. Over the years, many works have been conducted in the area of brain MR image segmentation methods for tumor detection. Automatic and semi-automatic approaches for brain tumor segmentation has been stated in the literature survey. Semi-automatic methods require user intervention for the brain tumor detection [3][4][5]. An expert radiologist performs decision making task with a significant degree of precision and accuracy. For greater accuracy and diagnostic abilities in the pathological classification of brain tissues, computer aided diagnosis systems have been developed [6][7][8][9][lO]. Segmentation methods includes a range of approaches based on classification using extracted features, level set methods, Markov random field (MRF) methods, fuzzy c-means (FCM), k-nearest neighbor (KNN) and region growing [11][12][13] [14][15][16][17][18][19] [20][21] [22][23][24]. Level Sets method requires initial curves identification. This method will yield poor results when there is asymmetrical placement of the curves with respect to object boundary. Heterogeneous tumors cannot be segmented using MRF since it is only applicable to homogenous tumors. KNN is very sensitive to irrelevant or redundant features and they have poor run-time performance. FCM requires huge computational time and produces unsatisfactory results in noisy images. Region growing methods separates the user defined regions with similar properties and its performance is better with noisy images Drawback of Region growing method is that it involves a manual seed point selection. Depending on predefined conditions, this method removes all pixels connected to the Preliminary seed. Considering the advantages and drawbacks of the above methodologies we propose a hybrid approach i.e. combination of region based and texture based methods for brain twnor detection and classification. First we segment the brain to remove skull from the image followed by symmetry property to identify the portion of the brain affected by the tumor region. Next we use Fast bounding box approach for detection of the location of tumor, which will act as input ROI for texture feature extraction step. Once the features have been
obtained, classification of tumor or non-tumor MR images is carried out. The results prove that the hybrid approach is having better detection and classification accuracy. Organization of the paper is as follows: section 2 deals with the related work in the field of brain tumor detection, section 3 describes the proposed methodology of detection of brain tumor from MR images. Experimental results are discussed in section 4, fmally concluding remarks are drawn in section 5. II. A.
schematic is shown in Fig. 2. The proposed methodology has four phases: preprocessing, GLCM based feature extraction, classification stage and segmentation. Details of these steps are presented below.
Fig. A.
RELATED WORK
FBB is a fast, automatic and an approximate segmentation technique which traces an axis parallel rectangle called as bounding box around the tumor. Change detection principle is used in FBB, where a region of change (D) is distinguished on a test image (I), when compared with a reference image (R). Once symmetrical axis is found on an axial MR slice, the right (or the left) section acts as the reference image R and the left (or the right) section behaves as the test image I. Region D is limited to be an axis parallel rectangle, which is the region of abnormality by considering both vertical and horizontal score functions defined by the Bhattacharya coefficient (BC).
B.
D denotes tumor containing region. T (I) is the top sub rectangle and B(I) is the bottom sub rectangle of the image. I is the midpoint of the image. Score function is used to fmd the best L y and U y values in a vertical sweep. (2) normalized intensity histogram of the
region T(I) in the test image. pJ (I), p,B(I), p: (I) are defined accordingly. E(I) is the score function,. Bhattacharya coefficient is defined as =
� �a(i)b(i) E [0,1]
Feature Extraction
Texture is an important feature for medical imaging analysis, since it provides the spatial distribution of pixels gray level in a region. Identification of object regions is performed by extracting the textural characteristics of that particular image by using various algorithms such as fractal based methods, markov random field and gabor filter. These algorithms are used for feature extraction of regions or region of interest in an image. Gray level co-occurrence matrix (GLCM) is a statistical based feature extraction method which is in the form of a matrix where number of rows and columns are equal to number of gray levels, G in the image. Numerous features are extracted from GLCM, the number of gray level is denoted by 'G'. GLCM analyzes image feature such as homogeneity, contrast, correlation, energy, etc. Texture energy, homogeneity, correlation and contrast are the selected four characteristic features for the identification of mean and variance of the disease's parameters.
(1)
BC(a , b)
Preprocessing
Feature extraction deals with minimizing the input data by determining the distinguishable features from several input patterns. The output of the feature extraction stage will be an input vector consisting of relevant image properties which will be fed into classifier.
As shown in Fig. 1(a), the rectangle region is defined as
p,T(l) denotes
Schematic of Proposed Methodology
Since MR images will be corrupted with the impulsive noise, removal of such noise is done by median filtering. Once the noise is filtered, the boundary detection criteria is used to detect the skull as shown in fig. 3(b).
Fast Bounding Box(FBB) [25]
Where
2:
�� �-� {P(i,j)}2
Homogeneity =
(3)
Where a(i) and b(i) are the two normalized histograms
CorreIatlOn · -
n-i-J n-i-J L;�o ��o
(4)
{i x J'} x P(i ' J') - {IIrx X IIry } (J
x
x (J
y
(5) (6)
Incr.
1
Deer.
Fig. I: (a) Bounding Box Detection (D); (b) Vertical Sweep Score Plot
III.
PROPOSED METHODOLOGY
In this section, a hybrid approach for brain tumor detection and classification has been proposed, whose
a
Fig. 163
3:
(a) Input Image; (b) Skull Detection
Energy =
JHomogeneio/
The dataset is a combination of a) Real time MRI scans obtained at Goa Medical College, Goa and b) Synthetic images which were accessible as a part of Brats challenge, MICCAI 2013 [28]. Pre contrast Tl- weighted axial image types and some post contrast Tl weighted axial images are considered in this work. The algorithm has been tested on high grade Glioma and low grade Glioma which are from the synthetic Brats database. lOO images have been used among which 75 are twnor MR images and 25 are normal MR images. Expert radiologist'S manual demarcation of tumor is considered as the ground truth. The algorithm is implemented in MATLAB 2014a with typical image resolution of 256*256 using a 2.2GHz, I3 windows OS machine.
The features obtained from feature extraction stage will be fed into the classification stage for classifying tumor and normal images.
C. Classification The key role of the classification stage is to categorize all pixels in an input image into normal or abnormal class depending on the features occurring in an image in terms of statistical parameters. Classification is divided into supervised classification and unsupervised classification. Supervised classification deals with inputting training set and then testing the input samples with respect to the training set and then classifying them. Whereas unsupervised classification does not necessitate training phase for classification.
LS-SVM classifier with two different kernel functions (RBF and MLP) have been implemented for the classification purpose. Tenfold cross validation has been used to train and test the classifiers. Among lOO images, 75 are tumor images and 25 are normal images. 30% of tumor images i.e. 23 and 30% of normal images i.e. 8, total of 31 images have been used to train the system and the remaining 69 images have been used for testing and validating the result.
Support Vector Machines (SVM) are supervised classification models which are used in learning algorithms for data analysis, pattern recognition, classification and regression analysis [26][27]. In this proposed work we have used LS SVM (Least Squares Support vector machines) classifier with MLP (Multi-layer perceptron) based kernel functions and RBF (Radial Basis Function) kernel functions for classification of the normal and abnormal datasets. D.
EXPERIMENTAL RESULTS
IV.
(7)
True positive, True Negative, False Positive and False Negative are the confusion matrix features that is used for measuring the specificity, sensitivity and accuracy of the classifier system.
Segmentation
Segmentation is a process of partitioning an image into dissimilar sections containing each pixels with similar features. Tumor segmentation is performed using FBB as shown in Fig. 4.
True Positive = . True Negattve
Number of resulted images having brain tumor Total number of images
Number of images without tumor =
False Positive =
(8) (9)
.
Total number of Images Number of images falsely det ected as tumor Total number of images
(10)
False Negative = Number of images having tumor and not detected
(11)
Total number of images
Accuracy= [
(True Positive
+
True Negative)
(True Positive + False Positive + True Negative + False Negative)
]
(12)
Classifier Performance measure is based on classification accuracy, i.e. nwnber of samples that has been properly classified into normal or abnormal classes. Diagnostic accuracy of imaging examinations is expressed by Receiver operating characteristic (ROC) analysis. ROC is used to demonstrate the binary classifier performance by varying its discrimination threshold. ROC plot is formed by plotting the Fig.
4:
Location of Tumor using FBB
164
true posItIve rate (sensitivity) against false posItIve rate (specificity) at different threshold limits. The area under Roe curve indicates the classifiers probability to correctly classify tumor cases and normal cases. The higher the AVe, the better the test. ROe curve has been shown for MLP kernel function and REF kernel function using LS-SVM classifier in the fig. 5.
a
b
b
a
Fig. 5: (a) MLP ROC Curve; (b) RBF ROC Curve
Sensitivity and specificity involves appropriate cutoff values determination for classification, whereas AVe is a one value measure of accuracy of test. Therefore AVe is considered as the basis for the statistical performance comparison of different models. Table 1 describes about the different classification parameters obtained by the kernel functions. From the table we can conclude that accuracy, specificity and area under curve of the MLP kernel function is better than the REF kernel function. Standard error is comparatively less in case of MLP kernel function. The experimental results of tumor location detection using FBB approach is shown in figure 6. Rectangle box in the figure 6(b),(d),(t),(h) denotes the tumor location. Table I. shows the existing techniques classification accuracies. As per our proposed methodology, classification accuracies using REF kernel and MLP kernel is shown in Table II. TABLE 1: CLASSIFICATION ACCURACIES OF EXISTING TECHNIQUES IN LITERATURE.
Method
Accuracy
Back Propogration neural network [29]
76.19%
Radial basis function networks r291
85.71%
AdaBoost classifier r301
90.11%
Support Vector Machines [31]
95.6%
TABLE 2: CLASSIFlCATION ACCURACIES OF RBF AND MLP KERNEL FUNCTIONS ACCORDING TO OUR PROPOSED METHODOLOGY
Kernel
Method
Specificity
Accuracy
Area Under Curve
Standard Error
h
g
RBF
LS-SVM
40%
61.8%
0.936
0.0233
Fig. 6: MR Scans Segmented using Proposed Method; (a),(c),(e),(g)
MLP
LS-SVM
81.33%
96.63%
0.9467
0.0210
Real Time Identical Scans; (b),(d),(f),(h) Tumor Identification using FBB Algorithm
165
[14] Vaidyanathan, M.
CONCLUSION
V.
Segmentation
Computer aided detection system has become major research topic in medical imaging and diagnostic radiology. Hybrid approach i.e. combination of region based and texture based methods for brain tumor detection and classification has been proposed. GLCM have been used as texture based method for feature extraction from the MR images. LS-SVM classifier along with MLP kernel function is used classify the tumorous and non-tumorous images. Fast bounding box algorithm is used as region based method for tumor segmentation. The proposed methodology is more efficient than the existing methods and segmentation accuracy is good. The proposed methodology shows an accuracy of 96.63%.
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