- Email: [email protected]
An optimized technique for brain tumor classification and detection with radiation dosage calculation in MR image
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An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image R Kalpana , P Chandrasekar PII: DOI: Reference:
S0141-9331(19)30323-0 https://doi.org/10.1016/j.micpro.2019.102903 MICPRO 102903
To appear in:
Microprocessors and Microsystems
Received date: Revised date: Accepted date:
18 June 2019 25 September 2019 1 October 2019
Please cite this article as: R Kalpana , P Chandrasekar , An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image, Microprocessors and Microsystems (2019), doi: https://doi.org/10.1016/j.micpro.2019.102903
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image Kalpana R1, Chandrasekar, P2 1
2
Dept. of ECE, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai – 600062. India. Email: [email protected], [email protected] , Dept. of EEE, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai – 600062. India.
Abstract The tumor cell leads brain to abnormal growth birth. The Brain Tumors (BT) leads to damage or affected brain if it is not predicted early stage and rectified properly with proper treatment. The proper treatment as advised by the physician need to be followed based on the size of the tumor and its position. The accurate finding of tumor position and size is the difficult task. In recent years to structures of the body in internal position had seen detailed, the Magnetic Resonance (MR) technique if the radiology used in medical imaging. This paper describes a novel approach for BT MLTS-HSO segmentation and extracts the features then classified with different classifiers (KNN, DSVM, NB, and RBFN) for MR BT images. The present approach is SVM, NN, and ANFIS where the seed point is selected on a scale based and detected the tumor region and also compute the performance metrics and radiation dosage. Thus the proposed system is the ability of the calculate size and position of the tumor. It has more accurate prediction of the required surgery and other therapy procedures. Keywords: MLTS-HSO, Features Extraction, Classifiers, Radiation Dosage. I. INTRODUCTION In recent years, the automatic brain tumor segmentation and comparison is the primary objective of many researchers with high accuracy for diagnostic. The tools include patient history, a brain scan, CT scan, MRI scan. MRI scan has a contrast between the different soft cells of the body than computed tomography (CT). The introduced of the first MR image was in 1973. It is a device in which the patient lies within a large, where the magnetic field is used to align the magnetization of some nuclei in the body, and radio [1] frequency fields to systematically of this magnetization. It occurs in advanced detection stages when the presence of the tumor has caused symptoms. Especially [2] the size of the tumor variations, important information find out the most effective therapeutic regimes including surgery, radio- therapy and chemotherapy. Predict factors of the tumor include how cells are growing, the dead of the brain tumor cells in the necrosis, if the cells are determined to a specific area, and how similar the cancerous [3] cells are to normal tissue. II. METHODOLOGY The method of the proposed system involves following steps: (a) Classification Phase, (b) Detection Phase, (c) Radiation Dosage Calculation Phase. These techniques are explained in detail. The flow diagram of the proposed method is as shown in the Figure 1.
1
Fig. 1. Flow diagram of the proposed system A. Classification Phase It consists of the stage of the pre-processing and enhancement, segmentation, extraction of the features, and classifiers. The input image is transformed to gray image using RGB2gray conversion function. The transformed [4] image is an intensity image. Gray image is sub-divided into small bins of 8X8 called as tiles. After CLAHE (Contrast Limited Adaptive Histogram Equalization) technique is applied. In this technique, clip limit is selected for histogram equalization. Histogram is calculated for each bin individually. For contrast [5] enhancement histogram of each region is transformed in such a way that its height did not exceed the selected clip limit. Harmony Search Optimization (HSO) algorithm is a swarm intelligent optimization algorithm [6-8]. The optimization problem for k-way interaction model can be expressed as the Eq. (1). Max f(x),X=(x1,x2,……xk)
2
(1)
Where, xi 1, 2, search is as follows:
, N , N is SNPs number. if i j , then xi x j . The algorithm of the harmony
Step 1: Parameter initialization: The control of harmony search parameters are determined, which include HMS, Harmony Memory Considering Rate (HMCR), Pitch-Adjusting Rate (PAR), fret width (fw) [9] and the final criterion. Step 2: Initialize the Harmony Memory (HM) and the fitness value of each harmony is calculated. For i=1: HMS Xi ; For j=1: k While a X i a rand (0,1) N ; End Xi a; End // calculating the fitness value Score(i) f ( X i ) ; End Where, rand(0,1) represents a uniformly distributed [10] between 0 and 1. The harmony memory (HM) consists of HMS harmonies, as follow in the Eq. (2).
X 1 x11 2 2 X x1 HM HMS HMS X x1
x12 x22
x1k xk2
x2HMS
xkHMS
Score(1) Score(2) Score(HMS)
Step 3: A new harmony X new . X new For j=1:k If rand (0,1)
If rand(0,1)
(2)
X idworst X new ; Score(idworst ) f ( X new );
End Where idworst is the index of the HM worst harmony. Step 5: Stopping criterion is obtained. If stopping [12] criterion is meet, result is terminated. Otherwise, Step 3 and Step 4 are repeated. In this HSO algorithm has used two Multi Level Thresholding (MLT) like otsu and kapur. Otsu thresholding [13] is to find the threshold of the weighted within-class variance is minimized. In kapur thresholding, the entropy method is used to [14] obtain the optimal threshold values and also nonparametric method. It is separated of the classes in the optimal value appropriately, the maximum entropy value as in the Eq. (3). J (th) =H 1c + H 2c
(3)
Where, H1 and H2 are entropies, c is the component of the image if c =1, 2, 3 RGB image and if c =1 grayscale image. B. Features Extraction The tool for wavelet is a powerful mathematical, and has been used [15-17] to extract the coefficient from MR images. The continuous wavelet transform of a signal x(t), square-integrable function, (t) is defined as in Eq. (4) and Eq. (5). ( )
W (a, b) =∫
( )
( )
(4) (5)
Where, Ψa,b is the mother wavelet by translation and dilation parameter. The Eq. (5) can be the Discrete Wavelet Transform (DWT) expressed as in Eq. (6). ∑( ( ) ( )) DWTx (n) ={ (6) ∑( ( ) ( )) PCA (Principle Component Analysis) is defined as an orthogonal linear transformation that transforms the data to a new coordinate system [18-20]. The variance of the second coordinate is as in Eq. (7). Each coordinate in PCA is called Principle Component. Ci=bi1(x1)+bi2(x2)+…+bin(xn)
(7)
Where, Ci is the ith principle component, bij is the regression coefficient for observed variable j for the principle component i and xi are the variables/dimensions. In this DWT-PCA method, there are used in three features [21] such as Intensity Histogram Features (IHF), Gray Level Co-Occurrence Matrix (GLCM), and Laws Texture Features (LTF). 4
C. Classifiers It has four classifiers. They are Dense Support Vector Machine (DSVM) [22], K-Nearest Neighbor (KNN) [23], Naive Bayes (NB) [24], and Radial Basis Function Network (RBFN) [25] are compared in this paper. 1. Detection Phase The detection phase consists of AD processing [26], CEP with RG method [27], feature extraction, and classifiers. 2. Radiation Dosage Calculation Phase It uses high-energy rays to treat radiation therapy. It works by radiation damaging the DNA inside cells to divide and reproduce. The goal of this therapy is to the dose to abnormal cells maximized and exposure to normal cells minimized. The radiation [28] is not immediate but over time is occurred to benefits. III. PERFORMANCE METRICS The following confusion matrix of classifying is obtained. There are twelve performance assessments. They are Accuracy (Acc), Sensitivity (Se), Specificity (Sp), Prevalence (Pr), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Likelihood Ratio Positive (LRP), Likelihood Ratio Negative (LRN), Agreement (Ag), Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF). All performance assessments are compared with SVM, NN, and ANFIS classifiers with detection phase and comparing only accuracy (Acc) measurement with four different classifiers like NB, DSVM, and RBFN with classification phase. The performance assessment of proposed system is analysed this formula of the following equations from Eq. (8) to Eq. (16). Sensitivity (Se) = TP/(TP + FN) = TP/nD
(8)
Specificity (Sp) = TN/(FP + TN) = TN/nC
(9)
Prevalence (Pr) = (TP + FN)/(TP + FP + FN + TN)= nD/n
(10)
Positive Predictive Value (PPV) = TP/(TP + FP) = TP/nP
(11)
Negative Predictive Value (NPV) = TN/(TN + FN) = TN/nN
(12)
Likelihood Ratio Positive (LRP) = Se/(1-Sp)
(13)
Likelihood Ratio Negative (LRN) = (1-Se)/Sp
(14)
Agreement (Ag) = (TP + TN)/n
(15)
Accuracy (Acc) = (TP+TN) / (TP+FN+TN+FP) 5
(16)
The proposed criteria including Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF) are used for performances. SmI is for the segmented relative to the total segmented region, in both the manual and the segmented image by the proposed system. The OlF and the ExF specify that have been normal and abnormal classified as tumor area, respectively. Similarity Index, Overlap Fraction, and Extra Fraction are obtained by given Eq. (17), Eq. (18), and Eq. (19). SI=
(17)
OF =
(18)
EF =
(19)
IV. RESULTS AND DISCUSSION The algorithm has been implemented in MATLAB, with reference to the datasets specified in Table 2 contains both normal and abnormal conditions for analysis. Table 2 Dataset specification MRI BT Dataset Benign Malignant
Total Images 60 60
Training Segment 20 20
Testing Segment 40 40
A. Pre-Processing Output The image of the input is taken in the RGB form. For pre-processing, first it is converted into gray scale component and applying CLAHE enhancement technique. The Figure 2 shows that the preprocessing output.
Fig. 2 Pre-processing output B. Segmentation Output The Figure 3 shows the segmentation output. After pre-processing, then apply MLTS-HSO segmentation technique to segment the enhanced MRI BT image. In this Fig. 15 B-Benign, MMalignant, OT-Otsu Thresholding, KT-Kapur Thresholding, PSNR-Peak to Signal Noise Ratio, SDStandard Deviation, and M-Mean.
6
Fig. 3. Segmentation output C. Features Extraction Result In this section, Intensity Histogram Features (IHF), Gray Level Co-Occurrence Matrix (GLCM), and Laws Texture Features (LTF) of segmented images are extracted. The Table 3 shows that the features extraction result. Table 3 Features extraction result Feature Extraction
IHF
GLCM
LTF
Me SD Ent Var Sm RMS CO CR En Ho Sk Ku IDM LEng1 LEng2
Benign OK 0.003634 0.0810386 2.81161 0.006571 0.953358 0.081111 0.251096 0.122785 0.811182 0.948491 2.000878 24.01827 3.630426 3.62E+09 4.9E+08
KT 0.00265 0.081075 2.777294 0.00657 0.937127 0.081111 0.261696 0.073832 0.820013 0.950096 2.029279 24.11751 0.755133 3.45E+09 4.84E+08 7
Malignant OT 0.002675 0.081074 2.696108 0.006568 0.937662 0.081111 0.181287 0.155721 0.79201 0.943424 0.513281 7.797782 0.185491 3.57E+09 4.58E+08
KT 0.001294 0.081108 2.75692 0.006575 0.879206 0.081111 0.191338 0.121375 0.795447 0.943689 0.537816 8.32301 -0.32312 3.42E+09 4.66E+08
D. Detection Output The Figure 4 shows the detection output. After AD processing, then apply RG method with CEP technique to detect the tumor region in the AD processing image.
Fig. 4. Detection output E. Classifiers and its Performance Outputs Table 4 Accuracy metric on different classifiers with classification phase BT Identification Classifiers DSVM KNN NB RBFN
Acc MLTS-HSO using OT MLTS-HSO using KT 60 60 66.3333 60 66.6667 83.333 98.75 98.75
Finally, calculate the performance metrics like Sensitivity (Se), Specificity (Sp), Prevalence (Pr), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Likelihood Ratio Positive (LRP), Likelihood Ratio Negative (LRN), Agreement (Ag), Accuracy (Acc), Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF). The Table 4 shows that the Accuracy (Acc) metric on different classifiers with classification phase. The Table 5 shows that the performance metrics on different classifiers with detection phase.
8
Table 5 Performance metrics on different classifiers with detection phase MRI Abnormal BT Detection Images Classifiers Se Sp Pr PPV NPV LRP Sample Image 01 LRN Ag SmI OIF ExF Acc Se Sp Pr PPV NPV LRP Sample Image 02 LRN Ag SmI OIF ExF Acc
SVM
NN
ANFIS
1.0000 0.8214 0.3913 0.7826 1.0000 5.6000 0 0.8913 0.878049 1.000000 0.277778 89.1304 % 0.8679 0.8448 0.4428 0.8263 0.8894 5.5934 0.1564 0.8550 0.841316 0.867857 0.195238 85.5034 %
1.0000 0.9333 0.4643 0.9286 1.0000 15.0000 0 0.9643 0.962963 1.000000 0.076923 96.4286 % 0.8045 0.9977 0.5530 0.9977 0.8049 344.3399 0.1959 0.8909 0.890748 0.804533 0.001889 89.0862 %
0.9936 0.9713 0.4868 0.9705 0.9938 34.6728 0.0065 0.9822 0.981932 0.993641 0.030207 98 % 0.8480 0.9902 0.5362 0.9901 0.8493 86.3045 0.1535 0.9139 0.913530 0.847970 0.008499 91.4 %
F. Radiation Dosage Result The Figure 5 shows the graphical representation of Dose Equivalent (DE) for MR BT images. In this Fig. 16, the graph shows that MR BT images for RAD minimum range is 0.05mGy to 1mGy and RAD maximum range is 0.1mGy to 2mGy. The Figure 6 shows the graphical representation of Effective Dose Equivalent (EDE) minimum range values for MR BT images. The Figure 6 shows the graphical representation of MR BT images for DE minimum range value 1mSv with RAD minimum range value (0.05mGy to 1mGy) versus EDE values for both male (0.077mSv to 1.54mSv) and female (0.064mSv to 1.28mSv). The Figure 7 shows the graphical representation of Effective Dose Equivalent (EDE) maximum range values for MR BT images for DE maximum range value 2mSv with RAD minimum range value (0.05mGy to 1mGy) versus EDE values for both male (0.154mSv to 3.08mSv) and female (0.128mSv to 2.56mSv).
9
Dose Equivalent - DE Graph (milliSv) 2 DE Min Range DE Max Range
Radiation Absorbed Dose - RAD (milliGy)
1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
2 4 6 8 10 12 14 16 18 Relative Biologic Effectiveness - RBE or Quality Factor - QF
20
Fig. 5. Graphical representation of Dose Equivalent (DE) for MR BT images DE Min Range Values Versus EDE Values
Effective Dose Equivalent - EDE (milliSv)
1.6 Male Female
1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
0.1
0.2
0.3 0.4 0.5 0.6 0.7 Dose Equivalent - DE (milliSv)
0.8
0.9
1
Fig. 6. Graphical representation of Effective Dose Equivalent (EDE) minimum range values for MR BT images DE Max Range Values Versus EDE Values
Effective Dose Equivalent - EDE (milliSv)
3.5 Male Female
3
2.5
2
1.5
1
0.5
0 0
0.2
0.4
0.6 0.8 1 1.2 1.4 Dose Equivalent - DE (milliSv)
1.6
1.8
2
Fig. 7. Graphical representation of Effective Dose Equivalent (EDE) maximum range values for MR BT images 10
V. CONCLUSION In this classification work, DSVM, KNN, NB, RBFN are applied as classifier. Finally, the Accuracy (Acc) performance of all these classifiers has been analysed and compared. From that result, RBFN classifier gives better classification rate (98.75%) when compared with all these classifiers. In this detection work, NN, SVM and ANFIS are applied as classifier in the field of medical image. In addition; the seed point required for CEP with RG method is selected automatically to detect the tumor region in MR BT images. Evaluation results of ANFIS approach based on similarity criteria (Accuracy (Acc) for sample images 1 and 2 are 98%, and 91.4%, respectively) indicates higher performance, when compared with the NN and SVM approach, especially in MR BT images, in tumor regions. Finally it is calculated for radiation dosage parameters like DE and EDE of MR BT images. In DE, there is a graph plotting against RAD with QF for DE values compared with minimum and maximum range. And EDE there is a graph plotting against EDE with DEmin and DEmax values and compared it. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, or chemotherapy, and radiotherapy procedures.
Conflict of Interest This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors.
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6. R.Guoand, S.M.Pandit, “Automatic threshold selection based on histogram modes and a discriminant criterion,” Machine VisionandApplications,vol.10, no.5-6,pp.331–338,1998. 7. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, pp. 1277–1294, 1993. 8. P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques, ”Computer Vision, Graphics and Image Processing,vol.41,no.2,pp.233–260,1988. 9. W. Snyder, G. Bilbro, A. Logenthiran, and S. Rajala, “Optimal thresholding: a new approach,”Pattern Recognition Letters, vol. 11, no.12, pp.803–809, 1990. 10. J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol.19,no.1 ,pp.41–47,1986 11. Z.W. Geem, J.H. Kim,and G.V. Loganathan,“A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no.2,pp.60–68,2001. 12. Diego Oliva, ErikCuevas, Gonzalo Pajares, Daniel Zaldivar, and Marco Perez-Cisneros, Multilevel Thresholding Segmentation Based on Harmony Search Optimization, Hindawi Publishing Corporation: Journal of Applied Mathematics Volume2013, ArticleID575414, 24pages. 13. N. Otsu, “A threshold selection method from gray-level histograms, ”IEEE Transactions on Systems, Man, Cybernetics, vol. 9, no.1, pp.62–66, 1979. 14. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram, ”Computer Vision, Graphics & Image Processing, vol. 29,no.3, pp.273–285,1985. 15. V.P.S. Naidu and J.R. Raol, "Pixel-level Image Fusion using wavelets and Principal Component Analysis," Defence Science Journal, VoI. 58, No. 3, May 2008, pp. 338-352 02008,DESIDOC. 16. Nirosha Joshitha J, R. Medona Selin,"lmage Fusion using PCA in Multifeature Based Palmprint Recognition,"lnternational Journal of Soft Computing and Engineering (IJSCE) ISSN: 22312307, Volume-2, Issue-2, May 20 I 2. 17. VPS Naidu, "Discrete Cosine Transform based Image Fusion techniques," Journal of Communication, Navigation and Signal Processing (January 2012) VoI. I, No. I, pp. 35-45. 18. Yong Yang,Dong Sun Park, Shuying Huang, and Nini Rao," Medical Image Fusion via an Effective Wavelet-Based Approach," Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 20 I 0, Article ID 57934 I, 13 pages doi:10.1155/2010/57934I. 19. M.A.Berbar, S.F.Gahe, N.A.IsmaiII,"lmage Fusion Using Multi Decomposition Levels of Discret Transform," 8 2003 The Institution of Electrical Engineers. Printed and published by lEE, Michael Faraday House, Six HiIIs Way, Stevenage, SG I 2AY. 20. Mr. Rajenda Pandit Desale, and Prof. Sarita V. Verma, Study and Analysis of PCA, DCT & DWT based Image Fusion Techniques International Conference on Signal Processing, Image Processing and Pattern Recognition 2013 [ICSIPR] l 978-1-4673-4862-1/13. 21. R.P. Velthuizen, L.P. Clarke, L.O. Hall, Feature extraction for MRI segmentation, J. Neuroimaging (1999) 85–90. 22. C. Cortes, and V. Vapnik, Support-Vector Networks. Machine Learning, 20(3), 273–297. Hingham, MA, USA: Kluwer Academic Publishers, (1995). 23. B. V. Dasarathy, Nearest neighbor (NN) norms: nn pattern classification techniques. (B. V Dasarathy, Ed.). IEEE Computer Society Press, (1991). 24. C. Chow, C. Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968; 14:462–7. 25. J. Hertz, A. Krough, R.G. Palmer, Introduction to the Theory of Neural Computation Addison Wesley, Redwood City, CA, 1991. 12
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Author Biography
R.Kalpana is a research scholar of Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India
Dr.P.Chandrasekar working as Associate Professor in Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India
14
An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image R Kalpana , P Chandrasekar PII: DOI: Reference:
S0141-9331(19)30323-0 https://doi.org/10.1016/j.micpro.2019.102903 MICPRO 102903
To appear in:
Microprocessors and Microsystems
Received date: Revised date: Accepted date:
18 June 2019 25 September 2019 1 October 2019
Please cite this article as: R Kalpana , P Chandrasekar , An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image, Microprocessors and Microsystems (2019), doi: https://doi.org/10.1016/j.micpro.2019.102903
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
An Optimized Technique for Brain Tumor Classification and Detection with Radiation Dosage Calculation in MR Image Kalpana R1, Chandrasekar, P2 1
2
Dept. of ECE, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai – 600062. India. Email: [email protected], [email protected] , Dept. of EEE, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai – 600062. India.
Abstract The tumor cell leads brain to abnormal growth birth. The Brain Tumors (BT) leads to damage or affected brain if it is not predicted early stage and rectified properly with proper treatment. The proper treatment as advised by the physician need to be followed based on the size of the tumor and its position. The accurate finding of tumor position and size is the difficult task. In recent years to structures of the body in internal position had seen detailed, the Magnetic Resonance (MR) technique if the radiology used in medical imaging. This paper describes a novel approach for BT MLTS-HSO segmentation and extracts the features then classified with different classifiers (KNN, DSVM, NB, and RBFN) for MR BT images. The present approach is SVM, NN, and ANFIS where the seed point is selected on a scale based and detected the tumor region and also compute the performance metrics and radiation dosage. Thus the proposed system is the ability of the calculate size and position of the tumor. It has more accurate prediction of the required surgery and other therapy procedures. Keywords: MLTS-HSO, Features Extraction, Classifiers, Radiation Dosage. I. INTRODUCTION In recent years, the automatic brain tumor segmentation and comparison is the primary objective of many researchers with high accuracy for diagnostic. The tools include patient history, a brain scan, CT scan, MRI scan. MRI scan has a contrast between the different soft cells of the body than computed tomography (CT). The introduced of the first MR image was in 1973. It is a device in which the patient lies within a large, where the magnetic field is used to align the magnetization of some nuclei in the body, and radio [1] frequency fields to systematically of this magnetization. It occurs in advanced detection stages when the presence of the tumor has caused symptoms. Especially [2] the size of the tumor variations, important information find out the most effective therapeutic regimes including surgery, radio- therapy and chemotherapy. Predict factors of the tumor include how cells are growing, the dead of the brain tumor cells in the necrosis, if the cells are determined to a specific area, and how similar the cancerous [3] cells are to normal tissue. II. METHODOLOGY The method of the proposed system involves following steps: (a) Classification Phase, (b) Detection Phase, (c) Radiation Dosage Calculation Phase. These techniques are explained in detail. The flow diagram of the proposed method is as shown in the Figure 1.
1
Fig. 1. Flow diagram of the proposed system A. Classification Phase It consists of the stage of the pre-processing and enhancement, segmentation, extraction of the features, and classifiers. The input image is transformed to gray image using RGB2gray conversion function. The transformed [4] image is an intensity image. Gray image is sub-divided into small bins of 8X8 called as tiles. After CLAHE (Contrast Limited Adaptive Histogram Equalization) technique is applied. In this technique, clip limit is selected for histogram equalization. Histogram is calculated for each bin individually. For contrast [5] enhancement histogram of each region is transformed in such a way that its height did not exceed the selected clip limit. Harmony Search Optimization (HSO) algorithm is a swarm intelligent optimization algorithm [6-8]. The optimization problem for k-way interaction model can be expressed as the Eq. (1). Max f(x),X=(x1,x2,……xk)
2
(1)
Where, xi 1, 2, search is as follows:
, N , N is SNPs number. if i j , then xi x j . The algorithm of the harmony
Step 1: Parameter initialization: The control of harmony search parameters are determined, which include HMS, Harmony Memory Considering Rate (HMCR), Pitch-Adjusting Rate (PAR), fret width (fw) [9] and the final criterion. Step 2: Initialize the Harmony Memory (HM) and the fitness value of each harmony is calculated. For i=1: HMS Xi ; For j=1: k While a X i a rand (0,1) N ; End Xi a; End // calculating the fitness value Score(i) f ( X i ) ; End Where, rand(0,1) represents a uniformly distributed [10] between 0 and 1. The harmony memory (HM) consists of HMS harmonies, as follow in the Eq. (2).
X 1 x11 2 2 X x1 HM HMS HMS X x1
x12 x22
x1k xk2
x2HMS
xkHMS
Score(1) Score(2) Score(HMS)
Step 3: A new harmony X new . X new For j=1:k If rand (0,1)
If rand(0,1)
(2)
X idworst X new ; Score(idworst ) f ( X new );
End Where idworst is the index of the HM worst harmony. Step 5: Stopping criterion is obtained. If stopping [12] criterion is meet, result is terminated. Otherwise, Step 3 and Step 4 are repeated. In this HSO algorithm has used two Multi Level Thresholding (MLT) like otsu and kapur. Otsu thresholding [13] is to find the threshold of the weighted within-class variance is minimized. In kapur thresholding, the entropy method is used to [14] obtain the optimal threshold values and also nonparametric method. It is separated of the classes in the optimal value appropriately, the maximum entropy value as in the Eq. (3). J (th) =H 1c + H 2c
(3)
Where, H1 and H2 are entropies, c is the component of the image if c =1, 2, 3 RGB image and if c =1 grayscale image. B. Features Extraction The tool for wavelet is a powerful mathematical, and has been used [15-17] to extract the coefficient from MR images. The continuous wavelet transform of a signal x(t), square-integrable function, (t) is defined as in Eq. (4) and Eq. (5). ( )
W (a, b) =∫
( )
( )
(4) (5)
Where, Ψa,b is the mother wavelet by translation and dilation parameter. The Eq. (5) can be the Discrete Wavelet Transform (DWT) expressed as in Eq. (6). ∑( ( ) ( )) DWTx (n) ={ (6) ∑( ( ) ( )) PCA (Principle Component Analysis) is defined as an orthogonal linear transformation that transforms the data to a new coordinate system [18-20]. The variance of the second coordinate is as in Eq. (7). Each coordinate in PCA is called Principle Component. Ci=bi1(x1)+bi2(x2)+…+bin(xn)
(7)
Where, Ci is the ith principle component, bij is the regression coefficient for observed variable j for the principle component i and xi are the variables/dimensions. In this DWT-PCA method, there are used in three features [21] such as Intensity Histogram Features (IHF), Gray Level Co-Occurrence Matrix (GLCM), and Laws Texture Features (LTF). 4
C. Classifiers It has four classifiers. They are Dense Support Vector Machine (DSVM) [22], K-Nearest Neighbor (KNN) [23], Naive Bayes (NB) [24], and Radial Basis Function Network (RBFN) [25] are compared in this paper. 1. Detection Phase The detection phase consists of AD processing [26], CEP with RG method [27], feature extraction, and classifiers. 2. Radiation Dosage Calculation Phase It uses high-energy rays to treat radiation therapy. It works by radiation damaging the DNA inside cells to divide and reproduce. The goal of this therapy is to the dose to abnormal cells maximized and exposure to normal cells minimized. The radiation [28] is not immediate but over time is occurred to benefits. III. PERFORMANCE METRICS The following confusion matrix of classifying is obtained. There are twelve performance assessments. They are Accuracy (Acc), Sensitivity (Se), Specificity (Sp), Prevalence (Pr), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Likelihood Ratio Positive (LRP), Likelihood Ratio Negative (LRN), Agreement (Ag), Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF). All performance assessments are compared with SVM, NN, and ANFIS classifiers with detection phase and comparing only accuracy (Acc) measurement with four different classifiers like NB, DSVM, and RBFN with classification phase. The performance assessment of proposed system is analysed this formula of the following equations from Eq. (8) to Eq. (16). Sensitivity (Se) = TP/(TP + FN) = TP/nD
(8)
Specificity (Sp) = TN/(FP + TN) = TN/nC
(9)
Prevalence (Pr) = (TP + FN)/(TP + FP + FN + TN)= nD/n
(10)
Positive Predictive Value (PPV) = TP/(TP + FP) = TP/nP
(11)
Negative Predictive Value (NPV) = TN/(TN + FN) = TN/nN
(12)
Likelihood Ratio Positive (LRP) = Se/(1-Sp)
(13)
Likelihood Ratio Negative (LRN) = (1-Se)/Sp
(14)
Agreement (Ag) = (TP + TN)/n
(15)
Accuracy (Acc) = (TP+TN) / (TP+FN+TN+FP) 5
(16)
The proposed criteria including Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF) are used for performances. SmI is for the segmented relative to the total segmented region, in both the manual and the segmented image by the proposed system. The OlF and the ExF specify that have been normal and abnormal classified as tumor area, respectively. Similarity Index, Overlap Fraction, and Extra Fraction are obtained by given Eq. (17), Eq. (18), and Eq. (19). SI=
(17)
OF =
(18)
EF =
(19)
IV. RESULTS AND DISCUSSION The algorithm has been implemented in MATLAB, with reference to the datasets specified in Table 2 contains both normal and abnormal conditions for analysis. Table 2 Dataset specification MRI BT Dataset Benign Malignant
Total Images 60 60
Training Segment 20 20
Testing Segment 40 40
A. Pre-Processing Output The image of the input is taken in the RGB form. For pre-processing, first it is converted into gray scale component and applying CLAHE enhancement technique. The Figure 2 shows that the preprocessing output.
Fig. 2 Pre-processing output B. Segmentation Output The Figure 3 shows the segmentation output. After pre-processing, then apply MLTS-HSO segmentation technique to segment the enhanced MRI BT image. In this Fig. 15 B-Benign, MMalignant, OT-Otsu Thresholding, KT-Kapur Thresholding, PSNR-Peak to Signal Noise Ratio, SDStandard Deviation, and M-Mean.
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Fig. 3. Segmentation output C. Features Extraction Result In this section, Intensity Histogram Features (IHF), Gray Level Co-Occurrence Matrix (GLCM), and Laws Texture Features (LTF) of segmented images are extracted. The Table 3 shows that the features extraction result. Table 3 Features extraction result Feature Extraction
IHF
GLCM
LTF
Me SD Ent Var Sm RMS CO CR En Ho Sk Ku IDM LEng1 LEng2
Benign OK 0.003634 0.0810386 2.81161 0.006571 0.953358 0.081111 0.251096 0.122785 0.811182 0.948491 2.000878 24.01827 3.630426 3.62E+09 4.9E+08
KT 0.00265 0.081075 2.777294 0.00657 0.937127 0.081111 0.261696 0.073832 0.820013 0.950096 2.029279 24.11751 0.755133 3.45E+09 4.84E+08 7
Malignant OT 0.002675 0.081074 2.696108 0.006568 0.937662 0.081111 0.181287 0.155721 0.79201 0.943424 0.513281 7.797782 0.185491 3.57E+09 4.58E+08
KT 0.001294 0.081108 2.75692 0.006575 0.879206 0.081111 0.191338 0.121375 0.795447 0.943689 0.537816 8.32301 -0.32312 3.42E+09 4.66E+08
D. Detection Output The Figure 4 shows the detection output. After AD processing, then apply RG method with CEP technique to detect the tumor region in the AD processing image.
Fig. 4. Detection output E. Classifiers and its Performance Outputs Table 4 Accuracy metric on different classifiers with classification phase BT Identification Classifiers DSVM KNN NB RBFN
Acc MLTS-HSO using OT MLTS-HSO using KT 60 60 66.3333 60 66.6667 83.333 98.75 98.75
Finally, calculate the performance metrics like Sensitivity (Se), Specificity (Sp), Prevalence (Pr), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Likelihood Ratio Positive (LRP), Likelihood Ratio Negative (LRN), Agreement (Ag), Accuracy (Acc), Similarity Index (SmI), Overlap Fraction (OlF), and Extra Fraction (ExF). The Table 4 shows that the Accuracy (Acc) metric on different classifiers with classification phase. The Table 5 shows that the performance metrics on different classifiers with detection phase.
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Table 5 Performance metrics on different classifiers with detection phase MRI Abnormal BT Detection Images Classifiers Se Sp Pr PPV NPV LRP Sample Image 01 LRN Ag SmI OIF ExF Acc Se Sp Pr PPV NPV LRP Sample Image 02 LRN Ag SmI OIF ExF Acc
SVM
NN
ANFIS
1.0000 0.8214 0.3913 0.7826 1.0000 5.6000 0 0.8913 0.878049 1.000000 0.277778 89.1304 % 0.8679 0.8448 0.4428 0.8263 0.8894 5.5934 0.1564 0.8550 0.841316 0.867857 0.195238 85.5034 %
1.0000 0.9333 0.4643 0.9286 1.0000 15.0000 0 0.9643 0.962963 1.000000 0.076923 96.4286 % 0.8045 0.9977 0.5530 0.9977 0.8049 344.3399 0.1959 0.8909 0.890748 0.804533 0.001889 89.0862 %
0.9936 0.9713 0.4868 0.9705 0.9938 34.6728 0.0065 0.9822 0.981932 0.993641 0.030207 98 % 0.8480 0.9902 0.5362 0.9901 0.8493 86.3045 0.1535 0.9139 0.913530 0.847970 0.008499 91.4 %
F. Radiation Dosage Result The Figure 5 shows the graphical representation of Dose Equivalent (DE) for MR BT images. In this Fig. 16, the graph shows that MR BT images for RAD minimum range is 0.05mGy to 1mGy and RAD maximum range is 0.1mGy to 2mGy. The Figure 6 shows the graphical representation of Effective Dose Equivalent (EDE) minimum range values for MR BT images. The Figure 6 shows the graphical representation of MR BT images for DE minimum range value 1mSv with RAD minimum range value (0.05mGy to 1mGy) versus EDE values for both male (0.077mSv to 1.54mSv) and female (0.064mSv to 1.28mSv). The Figure 7 shows the graphical representation of Effective Dose Equivalent (EDE) maximum range values for MR BT images for DE maximum range value 2mSv with RAD minimum range value (0.05mGy to 1mGy) versus EDE values for both male (0.154mSv to 3.08mSv) and female (0.128mSv to 2.56mSv).
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Dose Equivalent - DE Graph (milliSv) 2 DE Min Range DE Max Range
Radiation Absorbed Dose - RAD (milliGy)
1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
2 4 6 8 10 12 14 16 18 Relative Biologic Effectiveness - RBE or Quality Factor - QF
20
Fig. 5. Graphical representation of Dose Equivalent (DE) for MR BT images DE Min Range Values Versus EDE Values
Effective Dose Equivalent - EDE (milliSv)
1.6 Male Female
1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
0.1
0.2
0.3 0.4 0.5 0.6 0.7 Dose Equivalent - DE (milliSv)
0.8
0.9
1
Fig. 6. Graphical representation of Effective Dose Equivalent (EDE) minimum range values for MR BT images DE Max Range Values Versus EDE Values
Effective Dose Equivalent - EDE (milliSv)
3.5 Male Female
3
2.5
2
1.5
1
0.5
0 0
0.2
0.4
0.6 0.8 1 1.2 1.4 Dose Equivalent - DE (milliSv)
1.6
1.8
2
Fig. 7. Graphical representation of Effective Dose Equivalent (EDE) maximum range values for MR BT images 10
V. CONCLUSION In this classification work, DSVM, KNN, NB, RBFN are applied as classifier. Finally, the Accuracy (Acc) performance of all these classifiers has been analysed and compared. From that result, RBFN classifier gives better classification rate (98.75%) when compared with all these classifiers. In this detection work, NN, SVM and ANFIS are applied as classifier in the field of medical image. In addition; the seed point required for CEP with RG method is selected automatically to detect the tumor region in MR BT images. Evaluation results of ANFIS approach based on similarity criteria (Accuracy (Acc) for sample images 1 and 2 are 98%, and 91.4%, respectively) indicates higher performance, when compared with the NN and SVM approach, especially in MR BT images, in tumor regions. Finally it is calculated for radiation dosage parameters like DE and EDE of MR BT images. In DE, there is a graph plotting against RAD with QF for DE values compared with minimum and maximum range. And EDE there is a graph plotting against EDE with DEmin and DEmax values and compared it. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, or chemotherapy, and radiotherapy procedures.
Conflict of Interest This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors.
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Author Biography
R.Kalpana is a research scholar of Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India
Dr.P.Chandrasekar working as Associate Professor in Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India
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