Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images

© Springer International Publishing Switzerland 2015 A. Madevska Bogdanova and D. Gjorgjevikj, ICT Innovations 2014,

157

Advances in Intelligent Systems and Computing 311, DOI: 10.1007/978-3-319-09879-1_16

Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images

Emre Dandıl1,3,*, Murat Çakıroğlu2, and Ziya Ekşi3

1 Vocational High School, Dept. of Computer Tech., Bilecik Seyh Edebali University, Turkey [email protected]

2 Faculty of Technology, Department of Mechatronics Engineering, Sakarya University, Turkey [email protected]

3 Faculty of Technology, Department of Com. Engineering, Sakarya University, Turkey [email protected]

Abstract. Determination of the most suitable type of treatment for brain cancer depends on the accurate detection of the type, location, size and borders of the tumor. Computer-aided diagnosis(CAD) systems help to physician in order to facilitate realizing of these aims. In this study, a CAD system was designed to detect brain tumors with computer assistance using T1 and T2 weighted MR images. The designed system segments brain tumor region of MR image using spatial-Fuzzy C-Means(FCM) method. Also, features of tumor region are ex-tracted with image processing methods. Subsequently, support vector ma-chine(SVM) is used for classification of benign and malign tumors in the CAD system. According to detailed test results, the proposed CAD system recognizes brain tumors with 91.49% accuracy, 90.79% sensitivity and 94.74% specificity.

Keywords: Brain tumor, computer aided diagnosis, magnetic resonance imag-ing, classification, SVM.

1 Introduction

Cancer is spreading widely due to various reasons such as increased use of technolog-ical tools, consumption of unhealthy foods and rise in stressful events. It is estimated that cancer, second cause of death after cardiovascular diseases today, will become the main reason for deaths in the coming years. Based on data obtained from interna-tional scientific institutions such as American Cancer Society (ASCO) [1], rate of death caused by cancer is increasing rapidly in the whole world.

There are two types of brain tumors as benign and malignant. Benign tumors grow slowly and do not spread to neighboring tissues whereas malignant tumors grow rap-idly, are aggressive and may spread to other adjacent organs [2]. Brain tumors are among the leading type of cancers in recent years with seriously high contracting rates. Surgical operations, chemotherapy, and radiotherapy are used in the treatment of brain tumors [3]. The best type of treatment depends on physician’s accurate detection of the type, location, size and borders of the tumor. Therefore, it is crucial * Corresponding author.

158 E. Dandıl, M. Çakıroğlu, and Z. Ekşi

to detect the tumor as early and accurately as possible to administer appropriate treatment. Physicians detect the tumor by interpreting these MR images. However, it is highly problematic for physicians to discriminate, make decisions and provide di-agnosis in some cases. Misdiagnosis and wrong treatment methods create heavy fi-nancial burdens for the patient, reduce patient comfort and result in irremediable situ-ations. CAD systems that will contribute to physicians’ decision making processes are needed to minimize these obstacles.

Nowadays, CAD systems have been used as supplementary systems in the diagno-sis of several diseases. CAD systems which will particularly support the diagnosis of cancer have been very popular in recent years. By using MR images, Fletcher-Heath et.al. [4] proposed a method that allows automatic segmentation of brain tumors through the use of fuzzy c-means clustering method (FCM). Juang et.al.[5] suggested the use of k-means method for the segmentation of brain tumors. On the other hand, some studies mention the limitations of k-means method and emphasize that more successful results can be obtained through the use of fuzzy c-means clustering tech-niques (FCM) [6,7]. Artificial intelligence methods are also used in the detection of brain tumors. Reddick et.al. [8] proposed a two-phase system approach to discrimi-nate among the gray matter, white matter and other parts of the brain with Self-Organizing Maps (SOM) method by using MR images. SOM method was also uti-lized by Vijayakumar et.al. [9] to segment and rank brain tumors by using MR imag-es. In their study, Sachdeva et.al. [10] proposed a segmentation, feature extraction and classification based ANN system that detects brain tumors. Machine learning methods are used in the detection, segmentation and classification of brain tumors as well. In their study, Zacharaki et.al. [11] implemented the classification of brain tumors and identification of their phases with the support vector machine learning by using MR images. Arakiri and Reddy [12] proposed a computer aided system to detect and clas-sify the brain tumors with the help MR images.

Examination of literature about computer-aided diagnosis of brain tumors shows that in general, studies focus on identification of brain tumors. However it is evident that a holistic CAD system to differentiate between benign and malignant tumors, rank tumors, to monitor changes in tumors and implement patient management phases is absent. CAD software that can differentiate between benign and malignant tumors has been designed in this study to meet this need. Proposed CAD software can systematically detect benign/malignant tumors with high success rate.

2 Designed CAD System for Brain Tumors

The CAD system designed to detect brain tumors at early stages and classify them as benign and malignant is composed following phases. These phases includes (i) image pre-processing and enhancement, (ii) brain skull stripping and (iii) segmen-tation of the brain tumor, (iv) feature extraction (v) feature selection and (vi) classifi-cation of benign and malignant tumors. Block design of the CAD system is presented in Fig. 1.

Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images 159

Fig. 1. Block design of the CAD system designed to detect brain tumors

2.1 Image Pre-processing

In this phase of the CAD system, the images are pre-processed to enhance image quality and remove noise. The main goal of pre-processing process is to prevent mis-leading results that can occur in segmentation and classification processes. First, 3x3 median filter is applied to remove very small noise and parasites. Subsequently, his-togram equalization is used to remove roughness on MR images. Fig. 2 shows unpro-cessed brain MR images and MR images after pre-processing.

Fig. 2. Pre-processed brain images with brain tumor on MR images

2.2 Skull Stripping

This phase aims to strip the skull completely from the full brain image to eliminate the unnecessary regions [13]. In this phase, a modified novel thresholding approach (MNTA) is used to strip the skull. Skull section of the pre-processed MR image was found to have an average value between 0.2 (low) and 0.7(high) with this method. Algorithm 1 presents the steps of the thresholding method.

Image pre-processing

Image segmentation

Skull stripping

Feature extraction

Feature selection

Tumor classification

Benign

Malign


Algorithm 1. Double Thresholding Algorithm(MNTA)

Input: I(input image), lt(low threshold value), ht(high threshold value)

Output: I9(skull stripped image)

Procedure MNTA( I, lt, ht )

1: I2=convert I to double image

2: For i=1 : row length of I

For j=1 : column length of I

if (I(i,j)>lt and I(i,j)<ht))

I3(i,j)=1;

Else

I3(i,j)=0;

End If

End For

End For

3: I4 = convert I3 to binary image

4: I5 = erode I4 image

5: I6 = dilate I5 image

6: I7 = fill holes in I6 image

7: I8 = convert I7 to grayscale image

8: I9 = I*I8

9: Return I9

End Procedure

Fig. 3 presents some samples for skull stripping of pre-processed MR images by

MNTA. It's seen that the proposed method successfully strips the skull of brain MR images.

Fig. 3. Process of skull stripping in brain images

2.3 Image Segmentation

Image segmentation phase aims to segment the tumors on MR images. This study pro-posed spatial-FCM method to segment the brain tumors [14]. FCM feature analysis is an


unsupervised method used in image processing techniques such as clustering, medical imaging and target recognition [15]. Spatial information of the membership functions is used in spatial-FCM compared to traditional FCM method. Spatial function is defined as the sum total of membership functions of each neighboring pixel. Therefore, the FCM clustering method is made more sensitive. Fig. 4 presents the data on the brain images in the current study segmented with s-FCM.

Fig. 4. Segmentation of T1 and T2 weighted brain MR images using s-FCM

2.4 Feature Extraction

Following the segmentation of tumor regions, it is necessary to identify whether the tumor is benign or malignant. Although the decision is made with the help of classifi-ers, some identifying features on the tumor help the decision process. Therefore, tumor features on MR images that will present the characteristics of benign or malig-nant tumors should be extracted. In this study, feature extraction techniques with

Table 1. Feature extraction methods, extracted features and their numbers

Feature ext. method

Extracted features Num. of ext. features

First statistical features(FIF)

Standart deviation, Entropy, Mean, Skewness, Kurto-sis,Variance

6

Shape fea-tures(SF)

Circularity, Eccentricity, Area, BoundigBox, Centroid, FilledArea, ConvexArea, EquivDiameter, EulerNumber, Extent, Perimeter, Orientation, Solidity

16

Gray-level co-occurrence ma-trix(GLCM)

Angular Second Moment, Entropy, Dissimilarity, Contrast, Inverse Difference, Correlation, Homogeneity, Autocorre-lation, Cluster Shade, Cluster Prominence, Maximum probability, Sum of Squares, Sum Average, Sum Variance, Sum Entropy, Difference Variance, Difference Entropy, Information measures of correlation-1, Information measures of correlation2, Maximal correlation co-efficient, Inverse difference normalized, Inverse difference moment normalized

4x22=88

Number of total extracted features 110


methods such as first statistical features (FIF), gray-level co-occurrence matrix (GLCM) features and shape features(SF) were used to extract tumor features. FIF is used to obtain standard statistical information such as gray-level pixel value means and standard deviation in an image. SF is used to extract many geometric information such as the roundness, area and periphery of the shape [16]. GLCM is utilized to ob-tain detailed statistical information on a gray-level image [17,18,19]. 6 statistical, 16 shape and 88 GLCM features were extracted in the proposed CAD system. As a re-sult, a total of 110 different features were extracted from the ROI. Table 1 presents the feature extraction methods, extracted features and their numbers.

2.5 Feature Selection

Since 110 features extracted in this study are copious for purposes of classification, they may have negative effects on the decision period. It is crucial to select the most appropriate features from the original feature vector to increased classification accu-racy. Therefore, Principal Component Analysis (PCA) was used in the current study as a feature reduction method to reduce dimensionality. PCA is a statistical and gen-eral use feature reduction method used to reduce dimensionality of complex data en-tries composed of large pieces of information [20,21]. In this study, the most appro-priate 6 features were selected from 110 features obtained through feature extraction by reducing the dimensionality with PCA.

2.6 Tumor Classification

SVM is used in the classification phase of the proposed CAD system to differentiate between benign and malign tumors. SVM is an effective learning method used in classification problems [22]. A SVM classifier helps obtain the equation of the best linear classifier to separate the two categories. SVM uses kernel functions in separat-ing classes with large data. SVM provides better results in applications with less data with bigger dimensionality [23]. A SVM classifier structure that can discriminate between benign-malignant tumors were used in this study.

3 Experimental Results

An image database was created for the CAD system used in the study by collecting a total of 376 T1 and T2 based MR images (248 malignant and 128 benign) based on pathological results of 67 different patients. Tumor sizes changed between 3 and 45 mm. Images in the database were collected from 17 female and 50 male voluntary patients between the ages of 27 and 79. MR images were obtained from the MR equipment in Dr. Nafiz Körez Sincan State Hospital and Medpix, Department Of Radiology and Radiological Sciences [24]. All applications in the proposed CAD system were realized with MATLAB software. All experimental studies were under-taken by using a personal computer with 3.4 GHz i7 processor, 8 GB memory and Windows 7 operating system.

Computer-Aided Diagno

3.1 Detection Rates

50% (188) of the data in t50% (188) were used for nign/malignant tumor classPositive (TP), True Negativmors. In designed CAD systest data based on experime

Table 2. Obtai

CAD System

Positive(malign

Negative(benign

T

As Table 2 shows, whilebe malignant (TP) and 14 hand, 34 of the 36 benign were misclassified as maligtivity=TP/(TP+FN) and Spdetection performance of ththe performance results Acses approximately 91 cases

Fig. 5. Eva

Fig. 6 shows the ROC(Rtion accuracy of the designfine and reliably compare tthe ROC curve, the bigger obtained ROC curve points

0102030405060708090

100

Per

form

ance

(%

)

osis of Malign and Benign Brain Tumors on MR Images

the CAD system were used for training purposes whertesting. A confusion matrix was obtained to assess

sification performance. This matrix is composed of Tve (TN), False Positive (FP) and False Negative (FN) stem, Table 2 presents the confusion matrix obtained frental results using support vector machine classifier.

ned confusion matrix for test data in classification

Positive Negative Total

n) 138 14 152

n) 2 34 36

Total 140 48 188

e 138 of the 152 malignant brain tumors were identifiedtumors were misclassified as benign (FN). On the ot

tumors were identified as benign (TN) and only 2 tumgnant (FP). Accuracy=(TP+TN)/(TP+TN+FN+FP), Senpecificity=TN/(FP+TN) criteria were calculated to obthe proposed CAD system as a percentage. Fig. 5 prese

ccording to these results, the software successfully diagout of 100.

aluation of performance measurement criteria

Receiver Operating Characteristic) curve for the classifined CAD system. ROC curve is a preferred method to the accuracy of diagnostic tests. The higher the area un

the success rate of the classification system [25,26]. Tto high accuracy.

91.49 90.79 94.74

Accuracy Sensitivity Specifity

163

reas be-

True tu-rom

d to ther

mors nsi-tain ents gno-

fica-de-

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164 E. Dandıl, M. Çakıro

Fig. 6.

3.2 Detection Duration

Fig. 7 displays computationsegmentation may be relattechnique. The reason for based on the large amountsposed CAD system classifiof 1-2 seconds. This is a reneeds for a radiologist to m

Fig. 7. Comp

4 Conclusion and

This study proposes an autmors as benign/malignant uused on MRI images in ima

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ROC curve for the designed CAD system

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n times for each step in the CAD system. Longer timeted with the use of s-FCM method which is an iteratlarge times in feature extraction and selection phase

s of necessary mathematical operations. However, the pies a brain MR image as benign/malignant in a time fraeasonable time frame when it is compared with the tim

make decisions regarding the malignancy of the tumor.

putation times in the phases of the CAD system

Future Works

tomatic CAD system that successfully classifies brain using MR images. Main image processing techniques wage pre-processing phase. Additionally, brain tumors w

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

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False Positive Rate

skull stripping segmentation feature ext. and sel.

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segmented with the help of segmentation phase using s-FCM. FIF, SF and GLCM techniques were used for feature extraction and PCA method was used for feature selection. SVM method was used in the classification phase. The rates for accuracy, sensitivity and specificity were found as 91.49%, 90.79% and 94.74% respectively. Increasing the training database may provide better detection performance.

This study is a single step in an integrated CAD system that allows detection of tumors, differentiation of benign and malignant tumors, tumor ranking, monitoring the changes that occur in tumors and implementing patient management stages. In future studies, we will focus on integrating other features and design an integrated CAD platform. It is also planned to code the future CAD systems with open source code platforms such as ITK [27] instead of MATLAB to ensure reduction in the deci-sion period and provide more effective operations.

Acknowledgments. We would like to thank the authorities in Dr. Nafiz Körez Sincan State Hospital and Medpix[24] for their significant contributions to our image data-base by providing MR data. This work was funded by Sakarya University BAPK (No: 2014-50-02-015).

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Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images

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