An Efficient Feature Extraction Technique (EFET) for identifying tumor in Brain images

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1 Volume , ISSN: (on-line version) url: An Efficient Feature Extraction Technique (EFET) for identifying tumor in Brain images S. RATHNADEVI Research Scholar, PG and Research Dept. of Computer Science Periyar E.V.R. College (Autonomous) Trichirappalli, Tamil Nadu. Mail id. : rathnajjc@gmail.com Dr. T.N. RAVI Assistant Professor and Research Co-ordinator, PG and Research Dept. of Computer Science Periyar E.V.R. College (Autonomous) Trichirappalli, Tamil Nadu. Mail id. : proftnravi@gmail.com Abstract Extracting brain tumors features from digital images is a tedious and time consuming task. Also, the accuracy of clinical evaluations on brain tumors depend solely on the experience of clinical experts. Computer aided systems do help overcome these limitations, but again the resultant accuracy is reliant on the procedures followed in feature extractions. Feature extraction of brain tumor is the base for further processes like tumor classifications. This paper proposes a merged methodology in Efficient feature Extraction Technique (EFET), where an occurrence matrix and other important features are applied and evaluated for improving accuracies in brain tumor feature extractions from brain images. I. INTRO DUCTIO N Human brain is a complex organ with millions of neurons participating in a mutual network. Developments in brain exploration using generated images have been possible dut to tools. Researches on the brain has been on the rise recently as they can illuminate changes in the brain and its relation to cognitive, perceptual and social abilities. These studies attempt to understand the structure and functioning of the brain or identify anomalous structures in the brain. Neuro imaging can enhance early identification of brain abnormalities like tumors, where a tumor is an unwanted mass and its early detection is critical. Imaging modalities can be readily applied to achieve good images. Investigations based on imaging modalities like Magnetic Resonance Imaging (MRI) or CT scan images have begun to increase in frequency and in contrast, the use of electroencephalography (EEG) has decreased considerably. Thus, image Processing plays a primary role in many medical applications. Brain images acquired from imaging modalities may sometimes result in poor quality, due to various reasons like distortion in imaging systems, adverse external conditions at the time of image acquisition or lack of expertise of the operator involved in imaging. Figure 1 depicts different types of image impurities. A pre-processed image is segmented for extracting features. Feature extractions and classifications are based on the previous processes for confirming tumors in the brain. Segmentation divides an image into multiple parts for identifying objects of interest and extracting required relevant information in images. The division produces distinct regions with pixels having similar attributes. Segmentation is a lowlevel image processing transformation on greyscale or color images and produces multiple images which can be described in terms of features, objects, and scenes. Segmentation techniques exploit relationship between pixels in close spatial locations. Studies have addressed tumor identification 631

2 techniques from MRI images [8] [7] or other imaging modalities [5] [3] [6]. This study also addresses feature extraction of tumor areas in brain images by proposing a novel technique called Efficient feature Extraction Technique (EFET). than 16 seconds and with an error rate of Table 1 depicts the FCM segmentation output in terms of accuracy, when compared to K-Means. The segmented brain parts from images are then used by EFET for feature extractions. Table 1 FCM Segmentation Accuracy Table Algorithm Accuracy Error Rate Execution Time (Sec) K-Means FCM Figure. 1 Enhanced Brain Image II. SEGMENTATIO N O F BRAIN IMAGES Prior to extraction of features using IEFT, since accurate tissue segmentation in brain images is important for detecting tumors, fuzzy -means (FCM) clustering was used for segmenting brain mages. A variety of fuzzy techniques have been proposed in image segmentation [9]. Brain images mostly suffer from intensity inhomogeneity and correction of this intensity is a must for segmentation. FCM based methods can be modified by adding a spatial constraint in the FCM objective function of FCM and used intensity inhomogeneity correction. Fuzzy spatial relations were integrated to correct intensity inhomogeneity and segment brain images [11]. This work used FCM (proposed by Dunn and modified by Bezdec) as it can effectively cluster data. A general objective function which can group data into clusters in FCM can be where count, - cluster arrays, c - cluster centroid N pixel count U = - partition matrix, X k - kth pixel, and V - centroid of the cluster and = - distance between the pixels and V i (Cluster centre). - k th pixel ss fuzzy membership in cluster i. It has to satisfy the condition and (1, ) -weighing Parameter for every membership value and is set to 2 for increased for fuzzy clustering. When higher values are assigned to pixels, it minimizes the objective function near the centroid of its class, and lower membership values draw them away from the centroid. The accuracy of FCM segmentation on brain MR images was found to be executed in less III. EFET Image Texture exhibits image variations in terms of brightness, which is capitalized in brain images as different tissues display different pixel values in brain images [16]. Texture analysis in images can extract relevant properties needed for diagnostics of diseases or anomalies in digital images of the human body. Moreover, they can be numerically manipulated for retrieving quantitative measurements. Modelbased analysis methods depend on a mathematical model for predicting pixel values. Pixel intensity is used by Statistical methods for analysis. Structural approaches seek to understand the hierarchical structure of image objects. Transform approaches modify an image to obtain a new response image that is used in analysis [16]. The proposed EFET uses statistical approaches for analysis using first (GLRLM) and second orders (GLCM ) for extracting brain tumor features.. A. EFET First Order - Gray-Level Run-Length Matrix (GLRLM) EFET uses GLRLM, which is a two-dimensional matrix and where each element is the number of elements j with the intensity i, in an angle (θ). GLRLM searches the image, along a given direction for pixels with same gray level value on multiple runs. It computes the number of gray level runs with varying lengths [13]. The Run length is the number of adjacent pixels that have the same intensity in a specified direction. Run -length matrix measures for each run, the consecutive pixels with the same value and results in different run-length matrices for each image and for each chosen direction. The gray level run-length matrix is depicted in equation (1)...(1) w here Ng is Max. Gray level, Rmax is Max. length. Figure 2 depicts a sub image and its GLRLM Matrix in direction 0. EFET uses four directions in the run namely, 0, 45, 90 and 135º. 632

3 Fig. 3 GLCM Matrix of Four Gray Levels for θ = 0 and d = 1 Figure. 2. Image Matrix and GLRLM Matrix Table 2 depicts Five texture features extracted in EFET from GLRLM. Table 2 GLRLM Features In the above figure, since pixel-intensity of 0 neighboring 1 for θ = 0 has two occurrences, a matrix with value 2 in row 0 and column 1 is formed. This process is repeated for all values. EFET forms the GLCM matrixc using all the four possible values of θ. Moreover, EFET uses only 5 parameters namely Energy, Entropy, Contrast, IDM (Inverse Difference Moment), Directional moment(dm) in its GLCM calculations. Table 3 lists the feature set formed by EFET using GLCM. IV. EXPERIMENTAL ANALYSIS AND DISCUSSIO N EFET was implemented and evaluated using MATLAB. The intake 20 pre-processed images in normal and abnormal categories, Grey Level Run-Length matrix (GLRLM) and Gray Level Co-occurrence matrix (GLCM). Five GLCM features and Five GRLM features were extracted from the corresponding models respectively and are depicted in Table 3 and 4. Difference Table of EFET GLRLM and GLCM are listed in Table 5 and 6, while Table 7 lists the final output of EFET with probability of tumors listed based on extracted features of EFET. B. EFET Second Order Statistics - Gray Level Cooccurrence Matrix (GLCM) EFET uses GLCM to extract 2 nd order texture features [14] by modelling relationships between pixels and constructs a GLCM Matrix. A probability density function with conditions is used for estimating pixels that have similar gray values in a given direction for a chosen distance. The function can be depicted as p(i, j d, θ), where i and j are gray levels, θ can have values (0, 45, 90, 135) and d can be 1 to 5. A smaller value of d implies a coarse texture, whereas different values of d indicate fine texture. The values in the matrix have to be uniformly spread [16]. Textures that are coarser in one direction, the values spread about the main diagonal in the matrix will vary with the angle θ [9].. The GLCM matrix for 4 levels of gray in an image with d = 1 and θ = 0 is depicted in Figure 3. In GLRLM difference table 5, the values of , , , , , , , , , , , , indicate an abnormal growth. It can be seen that the non-uniformity values range between.02 to.04. In table 6 again values above 2 definitely indicate the presence of anomalous growth ( , , , , , , , , , ), Thus, it is evident from tables 5 and 6, that tumor or anomalous growth can be identified using EFET. The maximum and minimum classification accuracies for any classifier are between 80 and

4 Table 3 EFET GLRLM Feature Set Gray Run Long Level Length Short Run Image Runs nunifor Unifor Emphasis tage n- Runs Percen ID Emph Asis Mity Mmity Img Img Img Img Img Img Img Img Img Img E-04 Img Img Img Img Img Img Img Img Img Img Table 4 EFET GLCM Feature Set Image ID Energy Entropy Contrast IDM DM Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Table 5 EFET Difference Table for GLRLM Features Set RL GL nunifor Runs Short ID nuniformity Run % Long Runs Mity Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Table 6 EFET Difference Table for GLCM Feature Set Image ID Energy Entropy Contrast IDM DM Img Img Img

5 Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img ID GL nuniformit y RL nunifor mity Short Runs Table 7 EFET Feature extraction Final Output Run % Long Runs Energy Entropy Contrast IDM DM Mean Diff Img Img Info Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img Img IV. CO NCLUSIO N This paper has used MRI dataset for feature extractions. This paper has proposed and demonstrated the validity and purpose of a novel feature extraction technique after segmentation for prediction of anomalous growth in the brain from brain images. Though FCM has been used for segmentation, other classifiers can also be used in segmentation before applying EFET for extracting features of tumors from brain images 635

6 EFET technique can also be applied for feature extractions with real- and clinical-based cases. References [1] Anitha V. and S. Murugavalli, Brain tumour classification using two-tier classifier with adaptive segmentation technique, IET Computer Vision, 2016, vol. 10(1), pp [2] Riddhi.S.Kapse1, Dr. S.S. Salankar2, Madhuri.Babar3 Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journal of Electronics and Communication Engineering (IOSR- JECE) e-issn: ,p- ISSN: Volume 10, Issue 1, Ver. II (Jan - Feb. 2015), PP [3] Madhukumar S.and N. Santhiyakumari, Evaluation of k-means and fuzzy C-means segmentation on MR images of brain, Egyptian Journal of Radiology and Nuclear Medicine, vol. 46, no. 2, pp , [4] Sindhu A., S.Meera A Survey on Detecting Brain Tumor in mri Images Using Image Processing Techniques International Journal of Innovative Research in Computer and Communication EngineeringVol. 3, Issue 1, January 2015on Systems, Man, and Cybernetics, vol. 19, no. 5, pp [5] Demirhan A., M. Toru, and I. Guler, Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks, IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4, pp , [6] Kong Y., Y. Deng, and Q. Dai, Discriminative clustering and feature selection for brain MRI segmentation, IEEE Signal Processing Letters, vol. 22, no. 5, pp , [7] Kumari, R. SVM classification an approach on detecting abnormality in brain MRI images, International Journal of Engineering Research and Applications, vol. 3, pp , [8] S.Shanmugapriya, B.S.Sathishkumar, Recognition Of Human Identities Using Enhanced Knuckle Pattern Features, International Journal of Innovations in Scientific and Engineering Research (IJISER), Vol.1,.12, pp , [9] Guo L., L. Zhao, Y. Wu, Y. Li, G. Xu, and Q. Yan, Tumor detection in MR images using one-class immune feature weighted SVMs, IEEE Transactions on Magnetics, vol. 47, no. 10, pp , [10] Sakalauskas A., A. Lukosevicius1, and K. Lauckaite: Texture analysis of transcranial sonographic images for Parkinson disease Diagnostics ISSN ULT RAGARSAS (ULTRASOUND), Vol. 66,. 3, Pp [11] Masulli F. and A. Schenone, A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. [12] Colliot, O. Camara, and I. Bloch, Integration of fuzzy spatial relations in deformable models application to brain MRI segmentation, Pattern Recognition, vol. 39, no. 8, pp , [13] Mohamed, S.S. and Salama M.M. ( 2005 ) Computer Aided diagnosis for Prostate cancer using Support Vector Machine Publication: Proc., medical imaging conference, California, SPIE Vol. 5744, pp [14] Clausi D A. An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing Vol. 28(1). P [15] Jong Kook Kim et.al.,, Jeong Mi Park, Koun Sik Song and Hyun Wook Park Texture Analysis and Artificial Neural Network for Detection of Clustered Microcalcifications on Mammograms IEEE, pp , [16] Haralick R. M., Shanmugam K., Dinstein I. Textural Features of Image Classification. IEEE Transactions on Systems, Man and Cybernetics Vol. 3(6). P [17] Amadasun, M. and King, R., (1989) Textural features corresponding to textural properties, IEEE Transactions 636

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