Volume 118 No. 17 2018, 691-701 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Hybrid Approach for MRI Human Head Scans Classification using HTT based SFTA Texture Feature Extraction Technique Usha R 1 and Perumal K 2 Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, Tamil Nadu, India. usha.resch@gmail.com Abstract This paper proposes an HTT based SFTA texture feature extraction for analyzing the MRI (Magnetic Resonance Images) brain images and classify tumor and non-tumor levels. The new HTT (Hierarchical Transformation Technique) has been applied on MRI brain images for both images pre-processing and image enhancement. Then SFTA (Segmentation based Fractal Texture Analysis) texture features are fed into Naïve Bayes classifier for further image classification. The performance of powerful proposed method is also compared with GLCM, SFTA, WSFTA, and stationary wavelet based SFTA methods in terms of accuracy, sensitivity, and specificity. Key Words: magnetic resonance images, morphological operation, texture feature extraction, classification, top-hat transform, bottom-hat transform, segmentation based fractal texture analysis, Naïve B ayes classifier. 1 Introduction Today s medical image processing is a fast growing challenging field. 1 691
Medical imaging modalities are an important part of medicine. And that is also very helpful while planning and performing surgery. There are so many medical imaging modalities are available such as X-ray, MRI (Magnetic Resonance Image), Computerized Tomography (CT), Ultrasound (US) imaging. Among these modalities, MRI provides good quality images about human organs for both clinical and research process. The brain is a most important organ in the human body which commands human nervous system. Any problems in a brain cause the major issue even it may lead to human death. So the earlier stage analysis of brain issues that is very helpful for cure the brain tumor. A brain tumor occurs when abnormal or tumor cells are formed within the brain [1]. Automatic brain image classification gives very useful information for both diagnosis and surgery planning [2]. This paper focuses on to create a fully automated system for MRI brain image classification. 2 Literature Review The problem in successful pattern recognition is to extract efficient features which are a special form of dimensionality reduction. Generally, many techniques have been applied for extracting features from an image like spatial, transform and color based features etc. Among this texture-based feature has the powerful technique to differentiate normal tissues from abnormal tissues. Texture analysis is significant research field at computer vision, image processing, and pattern recognition. And texture also gives important characteristics about different image types such as biomedical modalities, natural scenes and remotely sensed data [3]. In order to acquire the image texture properties, texture analysis algorithm based on the usage of filter banks or GLCMs (Gray Level Co-occurrence Matrix) were employed to calculate textural characteristics of an image. These algorithms results in high computational cost. To solve this problem, an efficient feature extraction algorithm is required. Such an algorithm is the SFTA that is also reported in [4]. For extracting the prominent features from an MRI image, wavelet based SFTA feature extraction technique had been discussed [5]. DWT has a problem in wavelet coefficients translation invariant. To avoid this problem of DWT, stationary wavelet based SFTA was suggested for an efficient texture feature extraction [6]. In this paper, the HTT based SFTA texture feature extraction technique is used to achieve effective feature extraction in less number of iteration and naïve bayes classifier is applied to classify MRI brain 2 692
normal and abnormal images. 3 Image Processing Methods and Materials 3.1 Top-Hat Transform Top-hat transform is a mathematical morphological operation, which is used to extract the small elements and details of an input image. Typically, the top-hat transform can be defined as the difference between an input image and its opening by some structuring element [7]. The top-hat transform is given by the equation, ( ) (1) Here indicates the opening operation. 3.2 Bottom-Hat Transform Bottom-hat transform applies morphological bottom-hat filtering operation on input grayscale or binary image. It can be dually defined as the difference between the closing and input image [7]. The bottomhat transform is given by the equation, ( ) (2) Here indicates the closing operation. 3.3 Segmentation based Fractal Texture Analysis (SFTA) SFTA texture feature extraction algorithm is classified into two main phases, i. Initially, decompose the input gray scale image (I) by using Two-Threshold Binary Decomposition (TTBD) into a set of binary images and ii. Extraction of texture features in SFTA based upon the resulting binary images. TTBD is sub-divided into two levels to decompose the grayscale image into binary images. To decompose the input grayscale image I(x, y) into a set of binary images I b (x, y) based upon the threshold value set (T). That is given by the below equation, ( ) { ( ) (3) 3 693
Then next to select the pairs of thresholds from the threshold set (T) and apply those threshold pairs on the image I (x, y) for done the twothreshold segmentation. The Otsu algorithm is applied on each image region until the total number of the threshold is obtained. Here the total number of the threshold is defined by the user. TTBD incorporates the results of input gray level distribution to computes the thresholds set as described in multi-level Otsu algorithm. That is selecting threshold pairs as the following equation, ( ) { ( ) (4) Where I b (x, y), I g (x, y), t, t l and t u indicate the binary image, input gray scale image, threshold, lower threshold and upper threshold values in threshold set(t) respectively. During the second phase, the SFTA texture features are extracted from each obtained binary images such as mean gray level, size, and boundaries of fractal dimension. The regions boundaries of binary image I b (x, y) are described as an image border indicated by the symbol (x, y). It can be calculated as, ( ) { ( ) [( )] ( ) ( ) (5) Where N 8 [(x, y)] denotes the pixel set that is 8-connected to the position (x, y). If the value of (x, y) is 1, then the pixel at the position (x, y) in the corresponding binary image I b (x, y) has the value 0. Otherwise, the value of (x, y) is 0 [4]. 3.4 Naïve Bayes Classifier A Naïve Bayes algorithm is a simple probabilistic statistical classifier that computes probabilities set by counting the combination of values and frequency in a given image dataset. This algorithm applies Bayes theorem and assumes all features or attributes to be independent given value of the class variable. The NB assumption of this conditional independence actually holds, a naïve bayes classifier converges quicker other than logistic regression in machine learning problems [8]. 4 HTT based SFTA Texture Feature Extraction The proposed work is fully automated brain image classification method. An input MRI brain image has been pre-processed and enhanced by using top-hat and bottom-hat transformation. Then SFTA 4 694
texture features are extracted from those enhanced MRI images. Finally, these texture features are fed into naïve Bayes classifier which is applied to recognize the normal, abnormal MRI brain images. The different levels of MRI brain image classification are shown in below Figure1. Sta rt MRI brain scan Top-hat transform Bottom-hat transform Level1. HTT (Hierarchical Transformation Technique) Hierarchical Transformed Image Complement Texture feature extraction Features Level2. Texture Feature Extraction Naïve Bayes Classifier Normal Abnormal Level3. MRI Brain Image Recognition Sto Figure 1: Flow diagram for proposed work. A. Hierarchical Transformation Technique In level I, the collection of MRI brain images is pre-processed and enhanced by top-hat, bottom-hat transformation. The original input MRI image is shown in Figure.2 (a). The top-hat transform, which is used to maximize the contrast between the gaps and objects that separate them from each other as represented in Figure2.(b). Bottom hat performs a morphological operation on gray scale image which gives the difference between closing and input grayscale image as shown in 5 695
Figure2. (c).then add original image to top-hat image and then the bottom-hat image is subtracted from the resultant image is shown in Figure2. (d). lastly, complement method has applied to enhanced image based upon the intensity valleys as described in Figure2. (e). Figure 2:Hierarchical Transformation using Top-hat and Bottom-hat morphological operations (a) Original Image (b) Top-hat transformed Image (c) Bottom-hat transformed Image (d) Enhanced Image and (e) Complement Image B. Texture Feature Extraction Next in level-ii, SFTA texture features are extracted from the above hierarchical transformed brain image I h (x, y). The image I h (x, y) is decomposed into a set of binary images I b (x, y) depends upon the threshold values in the threshold set (T). The total number of threshold values (n t ) in the threshold set (T) is desired by the user. Figure 3: Two threshold binary decomposition of an enhanced image There are three texture features such as mean gray level, size and fractal dimension are extracted from each obtained binary images. Therefore, there are 30 texture features has been extracted from each binary images. 6 696
Figure 4: Sample extracted SFTA features from each binary images I b (x, y) (This id for 9 th iteration result) resulting from TTBD. C. Image Recognition Finally In level-iii, the proposed work recognizes input MRI brain normal and abnormal images by applying naïve bayes classifier. The testing object has been classified depends on the training samples and these texture features. The predictive accuracy, specificity, and sensitivity of proposed work are compared with other existing techniques. 5 Result and Discussion The proposed work of MRI brain image classification, the images in DICOM format was collected from the patients of Madurai Rajaji Government hospital, Tamil Nadu. There are 40 normal and 40 abnormal images available at collected MRI brain images for training set. Here 20 abnormal and 20 normal images are taken for testing set. 5.1 Statistical Measures To performance evaluation of this system, the following statistical measures such as confusion matrix, sensitivity, specificity, accuracy are computed. And this proposed method also compared with other existing methods like GLCM, SFTA, DWT based SFTA and SWT based SFTA. The confusion matrix delivers the four different possible outcomes to measure the quality of classification system of two classes 1 (normal) and 0 (abnormal). And those are represented in below Table 1. 7 697
Actual class Predicted Class Class Predicted Normal (1) Predicted Abnormal (0) Actual Normal (1) TP FN Actual Abnormal (0) FP TN Table 1: Performance of Confusion Matrix Where, True Positive (TP) occur when the number of subjects in class 1 well classified by the system. True Negative (TN) occur when the number of subjects of class 0 well classified by the system. False Positive (FP) occur when the number of subjects of class 0 that have been incorrectly classified by the system as class 1 when that is actually class 0. False Negative (FN) occur when the number of subjects of class 1 that have been incorrectly classified by the system as class 0 when that is actually class 1. The following equations are used to measure the performance of our proposed work such as, (6) Sensitivity and Specificity of the method measure the ability to identify abnormal and normal cases respectively. Accuracy or success rate is computed by sum of good detections such as TN and TP divided by N (TP+TN+FN+FP) number of samples. 5.2 Experimental Analysis The main objective of work is to recognize MRI normal and abnormal brain images with required features. So that the various texture classification analysis are investigated to differentiate normal and abnormal subjects of MRI brain images. And then the proposed work also compared with other existing methods using different statistical measurement that are represented in Table2. (7) (8) 8 698
In percentage Various Texture Feature Extraction Technique Actual Class Predicted Class GLCM Normal Abnormal Normal 60% 40% Abnormal 35% 65% SFTA Normal Abnormal Normal 65% 35% Abnormal 55% 45% Wavelet based Normal Abnormal SFTA Normal 70% 30% Stationary Wavelet based SFTA Abnormal 65% 35% Normal Abnormal Normal 65% 35% Abnormal 35% 65% Proposed Normal Abnormal Normal 72% 28% Abnormal 5% 95% Table 2: Average Performance of naïve Bayes Classifier for Various Texture Feature Extraction Methods In this above Table2, diagonally black shaded boxes represent the percentage of correctly classified brain images and the percentage of incorrectly classified brain images at the off-diagonal. Statistical Measures/ Sensitivity Specificity Accuracy Various methods GLCM 60% 65% 62.5% SFTA 65% 45% 55% Wavelet based SFTA 70% 35% 52.5% Stationary wavelet 65% 65% 65% based SFTA Proposed 72% 95% 83.5% Table 3: Classification Accuracy Rate in Percentage 100% 80% 60% 40% 20% 0% Sensitivity Specificity Accuracy Various Texture Feature Extraction Techniques Figure 5: Classification Results for Various Textures Feature Extraction Methods The above Figure5 represents the graphical representation of statistical measurement for different texture feature extraction techniques. 9 699
6 Conclusion In this paper, HTT based SFTA method has been developed for the best feature extraction. This proposed system has its uniqueness in terms of effective results in classification rate. According to this experimental outcome on MRI brain images naïve bayes classification method gives 83.5% accuracy. The performance of proposed work also compared with other existing texture feature extraction techniques. In future, we will use neural network classifier to improve the final system classification in best rates. The conventional SFTA takes less computation time for the extraction of texture image features. However, it takes maximum of computational time to detect the exact affected cells of brain tumor area. According to this reason, a new alternative approach is required to extract the perfect affected area in limited number of iteration for avoid the over segmentation at less computation time. In future, we will develop an alternative approach for an image segmentation approach with use of HTT technique based morphological image transformation techniques. Acknowledgement The authors would like to sincere thanks to Department of Radiology, Madurai Rajaji Government hospital, for their help about image database. References [1] NCI.2014-04-14,General Information About Adult Brain Tumors, Retrieved 8 June (2014). [2] Jayashri Joshi, Phadke, A.C, Feature Extraction and Classification in MRI, of IJCCT 2(2-4) (2010). [3] Verma B, Kullkarni S, Texture Feature Extraction and Classification, Springer vellag Berlin Heidelberg, CAIP 2001, LNCS 2124, 2001, pp. 228-235. [4] AlceuFerraz Costa, Gabriel Humpire)Mamami, Agmaluci Machado Traina, An Efficient Algorithm for Fractal Analysis of Texture,Pattern and Images (SIBGRAPI), (2012), pp. 39-46. [5] Saraswathi D, Srinivasan E, Sharmila G, An Automated Diagnosis System using Wavelet based SFTA Texture Features, ICICES2014, ISBN: 978-1-4799-3834 IEEE, (2014). [6] Usha R, Perumal K, An Automated Diagnosis of Magnetic Resonance Images for Brain Tumor using Stationary Wavelet based SFTA Texture Features, International Journal of Control Theory and Applications (IJCTA), ISSN: 0974-5572, 10(21) (2017), 17-24. [7] Suman Thapur, Shevani Garg, Study and Implementation of Various Morphology Based Image Contrast Enhancement Techniques, International Journal of Computing and Business Research, Proceedings of I-Soceity 2012, (2012), pp. 2229-6166. [8] Edwin Chen, Choosing a Machine Learning Classifeir, (2011). 10 700
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