Texture Classification of Brain

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1 Texture Classification of Brain Nikita Dubey Abstract In this paper, a new cluster-based approach is proposed for extracting features from the coefficients of a two-dimensional discrete wavelet transform. The wavelet coefficients from the matrix of each frequency channel are segregated into non-overlapping clusters in an unsupervised mode using a set of application-specific representative images. In practical situations, this set of representative images can be the same as the ones kept aside for training a classifier. The proposed method divides the matrices of computed wavelet coefficients into disjoint clusters that are centered around the position of dominant coefficients. The features that can distinguish images of one class from those of other classes are obtained by computing energies of the clusters. The feature vectors so obtained are then presented as input patterns to an image classifier, such as a neural network. Experimental results based on the applications for texture classification and wood surface defect detection have shown that the proposed cluster-based wavelet feature extraction method is able to effectively extract important intrinsic information content from the test images, and increase the overall classification accuracy as compared with conventional feature extraction methods Medical image is completely incomprehensible to untrained eye. Since Normal Human eye has limitation to discriminate the gray scale images. It has only discriminate pixel intensities up to gray levels. This restricts qualitative analysis of medical images. Hence we concentrate more on quantitative analysis that reveals more information from image. The presented contribution aimed at developing an automated imaging system which can efficiently classifies the normal tissues in medical images obtained from Computed Tomography (CT) scans. This paper presents texture feature based approach for Computerized Tomography (CT) scan images. A novel method of texture feature extraction based on Ridgelet transform has been reported in this paper. The approach consists of two steps: extraction of most discriminative texture features of regions of interest (ROI) and creation of classifier that automatically identifies the various tissues. The proposed algorithm validate against data obtained from different patients. Index Terms Texture Classification, Ridgelets, Computed Tomography I. INTRODUCTION Over past few decades, texture classification has received considerable interest in the various application areas from industrial automation to medical diagnosis etc. According to Sklansky (1978) An image region has a constant texture if a set of local properties in that region is constant, slowly varying or approximately periodic. Texture analysis is one of the most important techniques used in the analysis and interpretation of images. Texture analysis methods can be classified into four primary categories, namely statistical, geometrical, model-based and signal processing-based approaches [1].In medical imaging, texture is important because it is difficult to classify human organ tissues using shape or gray level information. Position, shape and size of organ from patient to patient. Size of organs may vary indifferent exposure slices of the same patient. Even by changing slice thickness, image information changes. Analysis of texture requires the identification of proper attributes or features that differentiate the textures for classification, segmentation and recognition. Various feature extraction and classification techniques have been proposed in the past for the purpose of texture analysis. Out of these methods, lots of approaches are based on Wavelet- based methods and gives good results due to its Multi-resolution analysis characteristics. The success of wavelet is due to good performance for pricewise smooth functions in one dimension. Unfortunately, these properties lost in two-dimension or higher. In essence, wavelets are good at catch zeroth-dimensional (point) singularities. However, 2-D piecewise smooth signals such as images always have first order and zeroth-order singularity. In addition, 2-D wavelet transform commonly uses separate wavelet basis, which is obtained by applying a 1-D transform separately in each dimension, so it is isotropic and lacks directional information which is a substantial aspect of describing the 1-D singularity [2]. Overcome this problem of wavelet transform in 1998, Donoho introduced Ridgelet transform [4]. Ridgelet transform is a new transform, which deals effectively with line or super-plane singularities. The paper is structured as follows: Section 2 describes the Ridgelet Transform; Section 3 discusses the overall methodology, including the classifier and performance measures. Section 4 presents results. II. LITERATURE SURVEY Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subfield of digital signal processing, digital image processing has many advantages over analog image processing; it allows a much wider range of algorithms to be applied to input data, and can avoid problems such as the build-up of noise and signal distortion during processing. Image segmentation refers to the process of partitioning a digital image into multiple regions (set of pixels). 28

2 The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in an image. In this thesis the various popular fuzzy techniques for image segmentation are studied. Various methods for better clustering and segmentation have been developed. The algorithms or methods developed are meant for online and real time applications like television, camera phone, etc. A. Fundamentals of Digital Image Processing Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels (derived from picture element),representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Digital image processing, either as enhancement for human observers or performing autonomous analysis, offers advantages in cost, speed, and flexibility, and with the rapidly falling price and rising performance of personal computers it has become the dominant method in use. An image is denoted by two dimensional functions of the form f(x,y). The value or amplitude of f at spatial coordinates (x,y) is a positive scalar quantity whose physical meaning is determined by the source of the image. In a digital image, (x,y), and the magnitude of f are all finite and discrete quantities,discipline, computer vision is concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multidimensional data from a medical scanner. In computer vision, the input is a digital image and the output is some representation of its interesting features. Image processing is often used in computer vision as a pre-processing step. Image processing is de fined as an area when both input and output are images. As a technological discipline, computer vision seeks to apply the theories and models of computer vision to the construction of computer vision systems. The organization of a computer vision system is highly application dependent. Some systems are stand-alone applications which solve a specific measurement or detection problem, while other constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on if its functionality is pre-specified or if some part of it can be learned or modified during operation. There are, however, typical functions which are found in many computer vision systems. B. Image Segmentation Segmentation of an image entails the division or separation of the image into regions of similar attribute. The basic attribute for segmentation is image amplitude- luminance for a monochrome image and color components for a color image. Image edges and textures are also useful attributes for segmentation. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image. Segmentation does not involve classifying each segment. The segmentor only subdivides an image; it does not attempt to recognize the individual segments or their relationships to one another. III. RIDGLET TRANSFOR Transform domain representation of any image is important in characterization of texture. The intensity variations of that image data is essential in analyze texture. Wavelet transform give us a very sparse and efficient representation for piece wise smooth signals having finite singularities. But wavelets are unable to represent discontinuities along edges or curves in images or object except in horizontal and vertical directions. To extract texture in an effective way, we have required a transform that is able to captures singularities along edges and lines. For this application Ridgelet Transform very good. A. Continious Ridgelet Transform The continuous ridgelet transform (CRT) defined from a 1D wavelet function oriented at a constant lines and radial directions. The CRT in R 2 is defined as CRT ( a, b, ) ( x) f ( x) dx, f 2 R a, b, Where ridgelets,, ( x) function. in 2D are defined using a wavelet ab 1/2,, 1 2 ab ( x) a (( x cos x sin b) / a). Where a is the scale, parameter b is location and this is oriented at angle, and constant along the lines x1cos x2sin cons tan t [5,12].The point parameter (x,y) of Wavelet transform is replaced by line parameter(b, ) in Ridgelet transform. Wavelets are able to detect object of point singularities while Ridgelet represents object with line singularities. Show in fig 1. IV. METHODOLOGY The algorithm consists of three steps: Segmentation of interested region of CT scan image, extraction of most discriminative texture feature of that region, and creation of classifier that automatically classify the tissues. 29

Data Set The algorithm was tested on data set of CT scans medical images of different patients taken from Jabalpur Hospital and Research Center, Jabalpur.

3 Data Set The algorithm was tested on data set of CT scans medical images of different patients taken from Jabalpur Hospital and Research Center, Jabalpur. The size of each image is having resolution level is 12 bit. The Data sat consists of total 500 images. There are five organs were segmented namely Heart, Liver, Kidney, Backbone and Abdomen. For segmentation of organs we use Active contour models algorithm [6] CT Scan images transform and an application of 1D wavelet transform. For calculation of radon transform, we calculate 2D fast Fourier transform of image and an application of 1D inverse Fourier transform on each of 32 radial directions of radon project. A 1D Haar wavelet was applied to each of the radial directions, for three levels of resolution. The following texture descriptors were then calculated for each radial direction and resolution level of the wavelet details: mean standard deviation, energy, and entropy. For each level of resolution and each radial direction energy, entropy, mean, and standard deviation were calculated. The discriminating power of the Segmentation Segmentation Ridgelet transform Texture Descriptors Now each slice was further cropped and a square sub-images, which contain only segmented area were generated. Fig 3 shows the segmented image of CT scan and cropped image. The whole data set is split in to a training set and testing set for cross validation of the algorithm. Feature extraction Classifier & Classification rules Fig.2 Flow chart of algorithm Once the CT scan images were pre-processed then we applied the ridgelet transform and then texture feature vector extracted. After applying multi-resolution transform first and second order statistics were calculated for classification. Finite ridgelet transform was applied to each of the images. For calculation of Ridgelet transform first we calculate the discrete radon Figure 1 Radial grid of the ridgelet transform following four feature vectors were investigated: energy and entropy signatures averaged over radial directions (), energy, entropy, mean, and standard deviation signatures averaged over radial directions (MS), energy signatures (), and entropy signatures (), neither averaged over radial directions. Each of these feature vectors was computed for three levels of resolution yielding six descriptors, 12 descriptors, and 96 descriptors respectively. 30

Input image Segmented image Fig.3 Segmented image of CT scan and cropped image from data base V. CLASSIFICATION There are two types of classification one is supervised and another is non-supervised.

Non-supervised learning is to identify cluster or natural groups in the feature space [7, 8].

4 Input image Segmented image Fig.3 Segmented image of CT scan and cropped image from data base V. CLASSIFICATION There are two types of classification one is supervised and another is non-supervised. Supervised learning is a process of designing a pattern classifier using a training set of patterns of known class. Non-supervised learning is to identify cluster or natural groups in the feature space [7, 8]. With the help of classification we able to classify the objects from feature space in to its output space after training. In general mapping converts data from one space to another space [3]. Using a training set a classifier can be trained such that it generalizes this set of examples of labeled objects into a classification rule. Classifier can be linear or non linear. Here we use Decision tree classifier based approach [10]. Decision tree classifier able predicts the class of an object from values of predictor variables. [11] The most relevant texture descriptors were found for each specific organ, and based on those selected descriptors, a set of decision rules were generated. These sets of rules were then used for the classification of each region. Using the C&RT cross-validation approach, each tree s parameter was optimized, including depth of tree, number of parent nodes, and number of child nodes. The depth of tree parameter is essentially the depth of the decision tree, and determines how much the tree will grow. The parent node is the number of possible roots per node, whereas the child node is the number of possible stems per root node. The parameters were considered optimal when the highest accuracy rate was found. To evaluate the performance of each classifier, specificity, sensitivity, precision, and accuracy rates were calculated from each of the misclassification matrices. A misclassification matrix is a table that lists each organ and the corresponding number of true positives, true negatives, false positives, and false negatives, respectively. From the misclassification matrix specificity, sensitivity, precision, and accuracy statistics are computed. Specificity is defined by Specificity = Sensitivity = Precision = Accuracy = No. of true Cropped Negatives image No of true negatives + No of false positives No. of true Positives. No. of Positives + No of false negatives No. of true positives No of true positives + No of false positive No of true positive + No of true negative Total sample Specificity indicates how well negative cases are handled. This is related to true negatives of all the negative cases in the dataset. Specificity measures the accuracy among positive instances; sensitivity measures the accuracy among negative instances. Precision measures show how consistently the results can be reproduced, and accuracy reflects the overall correctness of the classifier. Specificity and Sensitivity are the most important performance measures in the domain of medical images. 31

5 Redglet-based descriptions sensitivity specificity Precision Accuracy Organ Descriptor Heart MS Liver MS Kidney MS Backbone MS 87, Abdomen MS VI. RESULT AND CONCLUSION Results show that ropy signature gives better performances over all other features vectors. An analysis of the discriminating power of the entropy feature vector, based on the various resolution levels, was also carried out. The following sets of descriptors were calculated: 32

6 based on individual levels of resolution for all three level, based on two levels(l12), and based on three levels (L123). The results clearly indicated that individual resolution levels did not have sufficient discriminating power, thus multiple resolutions were needed. The table-1 contains the results for L123 on each of the four feature vectors. ropy is a statistical measure of randomness that can be used to characterize the texture of the input image. ropy measures the information in the signal[13,14,3].ropy is equal to [12] Petrou, M. Sevilla, P.G. Image Processing Dealing with Texture, John Wiley & Sons,Ltd., 2006 [13] Lew M. S., Principles of Visual Information Retrival, Springer, 2001 N ropy = p[ i, j]log p[ i, j] i M j The entropy is expected to be high if the gray levels are distributed randomly throughout the image.the accuracy rates of energy () were significantly lower 87-92%. The low performance of energy and entropy (),having 78 89% accuracy, and energy, entropy, mean, and standard deviation (MS), having 80 91% accuracy. In this paper the characterization of texture based on energy and entropy feature of Ridgelet transform of C.T. Scans images has been carried out. ropy gives batter performance with compare to Energy. REFERENCES [1] Tuceyran M. and Jain A. K., Texture analysis, in Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., Chapter 2.1, (1998),pp [2] Xia Junjun, Ni Lin, Miao Y., A new digital implemantation of ridglt transform for images of dydic length. Jouneral of Network and computer application,30(2007),pp [3] Ratnaparkhe,V.R., Manthalker,R.R., Joshi,Y.V., Texture characterization of CT images based on Ridgelet Transform. ICGST-GVIP Journal, ISSN x, Vol.(8),issue(V), January [4] Donoho D., Ridge function and orthonormal ridglets, J. Approx. Theory, 111(2)(2001) [5] Do,M.N., Vetterli, M, The finite ridgelet transform for image representation. IE Trans. Image Process, 12(2003) [6] Xu, D.H.,Lee J.,Raicu D.S., Furst J.D., Channin D.,. Texture classification of normal tissues in computed tomography. The 2005 Annual meating of the society for computer Application in Radiology. Orlando. FL, USA [7] Chaudhari,D. IP for target detection classification, STTP on image processing Applications, June [8] A.K.Jain, Fundamental of Digital Image Processing, PHI July [9] R.O.Dudo, P.E. Hart, D.G.Stork. Pattern classification II Eductation, Wiley Interscience, John wiley & sons INC publication [10] Channin, D. Raicu, D.S. Furst,J.D. Xu, D.H., Lilly Limpsangsri, L. C.Classification of tissues in computed tomography using decision trees, The 90th Scientific Assembly and Annual Meeting of Radiology Society of North America, [11] Dettori, Lucia, Semler, Lindsay. Acomparison ofwavelet, ridgelet, and curvelet-based texture classification in algorithms computed tomography. Computers in Bilogy in Medicine 37(2007) pp

A COMPARISON OF WAVELET-BASED AND RIDGELET- BASED TEXTURE CLASSIFICATION OF TISSUES IN COMPUTED TOMOGRAPHY

A COMPARISON OF WAVELET-BASED AND RIDGELET- BASED TEXTURE CLASSIFICATION OF TISSUES IN COMPUTED TOMOGRAPHY Lindsay Semler Lucia Dettori Intelligent Multimedia Processing Laboratory School of Computer Scienve,