CLASSIFICATION OF SPECIFIC PATTERNS ALLUDING CANCER PRESENCE IN PROSTATIC MRI IMAGES

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1 International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 9, Issue 6, November-December 2018, pp , Article ID: IJARET_09_06_012 Available online at ISSN Print: and ISSN Online: IAEME Publication CLASSIFICATION OF SPECIFIC PATTERNS ALLUDING CANCER PRESENCE IN PROSTATIC MRI IMAGES Prajith Prakash Nair Centre for Advanced Research in Imaging Research Scholar, Jain University, Bangalore Dr.T.R.Gopalakrishnan Nair, FIE Centre for Advanced Research in Imaging Rajarajeswari College of Engineering, Bangalore, India ABSTRACT Several attempts in medical image processing have been made to detect physiological anomalies using various theoretical models.a standard imaginary of suspected regions of the body may have several types of anomalies corresponding to different causes. Detecting most specific ones bearing possibilities of cancer presence has continued to be a challenge. Neural network has been used for classification of images into cancerous and non cancerous types. In this paper we attempt to implement and classify Gabor filtered prostate MRI images using back propagation neural network for identifying tumorous and non tumorous images. Keywords: Gabor Transforms, Gabor Filters, Parameters To Tune Gabor Filter, MRI, Fuzzy C means, Back Propagation Neural Network. Cite this Article: Prajith Prakash Nair and T.R.Gopalakrishnan Nair, Classification of Specific Patterns Alluding Cancer Presence In Prostatic MRI Images, International Journal of Advanced Research in Engineering and Technology, 9(6), 2018, pp INTRODUCTION Cancer has been a major cause for human death in the past decade. It refers to abnormal division of cells in uncontrolled manners that result in the destruction of the human tissue. Many techniques such as MRI imaging, CT scanning have been devised to analyze such abnormal growth. Many types of techniques have been used for classifying such images into tumorous and non tumorous images. Most of these techniques have been successful to a certain extent in classifying the images. Even with such advancement we have to rely on invasive techniques to identify the malignant and non malignant tissues. In recent years the artificial neural networks (ANN) have been successfully used for activities such as analyzing samples, classification of various images according to a certain set of rules. Such types of editor@iaeme.com

2 Prajith Prakash Nair and T.R.Gopalakrishnan Nair systems can be modeled in a manner to produce a non invasive technique for classifying the MRI images into benign or malignant tumor images. In this paper the results of an extensive study of images from the organ prostrate of human body is presented and the techniques used are explained. Neural networks are type of systems that are designed to perform activities in a manner that is similar to the working of a human brain. The ANN typically is made up of multiple layers of simple processing units called neuron. These artificial neurons are interconnected to each other. This interconnection is based on certain level of coefficients of connectivity. The learning is accomplished by adjusting the strength of these coefficients to make the overall network to produce a desired output. Each of the artificial neuron is a simulated version of the biological neurons and consists of a node, connection, weight and node output that can be considered to represent the various parts of biological neural network such as dendrites, axon and axon terminals. This paper represents a novel technique wherein a probabilistic neural network is produced to classify the given prostate MRI image into different stages of prostate cancer according to Jewet -Whitmore staging criteria as shown in table 1. The classification of the tumor is done based on the character of the cell and the extension of the metastasis. This system classifies the tumor into 4 stages where the first two stages are considered to be curable. Stages C and D are treatable but do not produce a long time solution. 2. RESEARCH WORKS Prostate cancer has been identified as the major type of cancer causing death in men. Early detection and identification of prostate cancer will help to contain the spread of the disease to other organs. Several attempts have been made to classify the stage of cancer based on histological images. One of the earliest attempt to classify the images was done based on textural analysis of the images obtained (3). The most common type of grading system known as Gleeson grading system was suggested by W. Allsbrook, K.A. Mangold, et al with respect to inter observability (4),(11).Such type of manual grading is dependent on the inter observer variations which is both time consuming and error prone. TABLE 1 The Jewet Whitmore prostate staging classification. JEWETT WHITMORE CONFINEMENT DESCRIPTION A Organ Confined No distinction B Organ Confined Localized to a particular side C Seminal vesicle invasion Extra capsular extension is present D1 Loco regional Adenopathy Microscopic and macroscopic metastases D2 Far Spread Spread to other parts of body A computer aided diagnostic system for automatically distinguishing the images based on Gleason grades was suggested by Doyle.et.al.(5). The most common type of problem faced by researchers is to segregate prostate cancerous images due to the appearance of inflammation as suggested by A. Sciarra, G. Mariotti, S. Salciccia et al., (2). 3. PROSTATE CANCER GRADING TECHNIQUES The prostate is the male reproductive gland with an average size similar to the size of a chestnut located in the groin. It is composed of glands, muscles tissue and fibre.the gland is covered by a membrane known as the prostate capsule. The cancerous tumor appearing in prostate glands are classified based on the character of the cells and the extension of metastasis. There are basically two types of prostate grading systems, TNM system and the Jewett Whitmore system editor@iaeme.com

3 Classification of Specific Patterns Alluding Cancer Presence In Prostatic MRI Images The TNM (Tumor, Nodes and Metastasis) system basically follows the staging method of classifying the images into five main stages namely T0, T1, T2, T3 and T4 based on the spread of the tumor in the body.t0 stage refer to the stage where there is no evidence of primary tumor in the MRI image.t1 stage refer to the stage where there is presence of tumor in the gland but is not palpable or visible by imaging.t1 refer to the stage where there is a palpable difference between the non tumorous and tumor cells.when the patient undergoes biopsy at this stage an elevated level of prostate specific antigen is present.t2 refer to the stage where the tumor is confine within the prostate but is present in both the lobes. The tumor appears to extend through the prostatic capsule and is also present in the prostatic apex at stage T3.T4 stage represents the condition where the tumor is fixed or invades adjacent structures other than the seminal vesicles. The Jewett-Whitmore staging system is a simpler staging technique which classifies the type of cancer into four main categories as shown in table 1. Stage A refers to the initial stage of the tumor cells. In this condition the tumor cannot be differentiated from the normal cells in an MRI image. Stage B refers to the condition where the images show a palpable difference between the normal cells and the tumor cells. In this stage also the tumor is confined to the prostate gland. Stage C refers to the condition where the cells are found outside the prostate capsule.the spread of the tumor is limited to the surrounding tissues and seminal vesicles. Stage D refers to the condition where the cancerous cells have spread to various lymph glands, bones, organs (liver, lungs). The Gleason grading system is another type of cancer grading technique where the histopathology images are graded. This is the most common type of grading technique that is used for classifying the histological images (1-5).The Gleason grading system is used to measure the aggressiveness of the prostate cancer tumor but is only used for adenocarcinoma in grades 1 and 2 the cancer cells look exactly like the normal tissues and hence is not commonly used.most tumor are grade 3 or higher. Table 2 The Gleason grading System.. GRADE DESCRIPTION 1 No distinction from normal tissue 2 No distinction from normal tissue The cancer cells are well differentiated,the tissue would have invaded 3 surrounding areas of the gland 4 The cancer cells are between grade 3 and grade 5 The cells are differentiated due to abnormal growth and don t act like normal 5 cells. 4. ARTIFICIAL NEURAL NETWORK Artificial neural network refers to a system of nodes grouped together to form a network that performs task similar to the way the human brain works. Neural networks have been used for various activities in image processing such as classification and segmentation purposes. Classification with the help of artificial neural network has been proposed in (12-16). Figure 1 represent a basic ANN structure editor@iaeme.com

4 Prajith Prakash Nair and T.R.Gopalakrishnan Nair Figure 1 A Model of Artificial Neural Network There basically two main types of artificial neural network based on the type of learning Supervised learning ANN Unsupervised learning ANN A supervised learning artificial neural network basically refers to the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples which is used for training the network which also consists of a set of input and desired output. A supervised learning algorithm analyzes the training data and produces a function based on the characteristic of the data. This function is then used to classify the input into the appropriate classes. Unsupervised machine learning is the task of inferring a function from a category that is not provided in training data set. In this type of learning technique the amount of accuracy cannot be evaluated properly. 5. EXPERIMENTAL METHOD The basic steps involved in analyzing and classifying the images is as shown below IMAGE PRE PROCESSING IMAGE SEGMENTATION USING GABOR FILTER IMAGE FEATURE EXTRACTION CLUSTERING USING C MEANS ALGORITHM IMAGE ANALYSIS AND CLASSIFICATION USING BACK PROPAGATION NEURAL NETWORK Figure 2: Basic steps for prostate image analysis editor@iaeme.com

5 Classification of Specific Patterns Alluding Cancer Presence In Prostatic MRI Images 5.1. IMAGE PREPROCESSING The basic MRI images of prostate cancer are obtained from the prostate MR image database. The selected MRI image is subjected to preprocessing techniques to improve the quality of the image. The image is smoothed using a smoothing filter. The given prostate MRI images are first analyzed and then normalized. The normalized images are then subjected to the Gabor filter bank. The Gabor filter bank (10) consist of nine orientations and eight frequency orientation.each result is analyzed and the best combination of Gabor filter components is selected that will help to achieve the best possible segmentation of the MRI image. Figure 2 gives a basic functional block diagram of the above process. Edge sharpening of the image is achieved by passing the image through the Gabor filter. The figure 3 shows the output of a gabor filtered image. The filter is having an angular value of 180 degrees at a bw value of 1.5Hz. a) Input Image b) Edge Enhanced Image c) Filtered Image Figure 3 A Gabor filtered MRI image 5.2. IMAGE CLUSTERING The image clustering technique involves two main steps Image feature extraction C means clustering The image feature extraction involves the process of identification of an object. Morphology, variation in texture and edges are the features that are measured and extracted. It aims to quantitatively describe the shape of the objects in an image. It is analyzed by combining the image to a small object called structuring elements. The structuring element is scans the image and modifies it according to a specific rule. The pattern spectrum operator helps in decomposing the target image into smaller components according to the shape and size of the structuring element. The images obtained after the filtering is then subjected to segmentation using an automatic clustering technique called the Fuzzy c means technique. Clustering is basically a editor@iaeme.com

6 Prajith Prakash Nair and T.R.Gopalakrishnan Nair technique that allows to group together several pixels of the image into different groups based on their similarities. It is basically based on minimization of the objective function Where m is any real number greater than 1, the fuzzy partitioning is done through an iterative optimization of the objective function. The cluster centers are given by the equation The basic Fuzzy C means Algorithm involves the following steps Initialize U = [u ij ] matrix, U (0) At the K th step calculate the center vectors C (k) Increment the value of U (k) to U (k+1) If {U (k+1) - U (k) }< Ԑ then stop, else return to step 2 In the above algorithm the degree of membership between each pixel and the center of the clusters is determined by the matrix U. The figure 4 denotes the various cluster formed after performing the c- means algorithm. Both the sine as well as the cosine characteristics of the filter is convolved together to obtain a prominent result. a) Input Image b) Cluster 1 b) Cluster 2 d) Cluster editor@iaeme.com

7 Classification of Specific Patterns Alluding Cancer Presence In Prostatic MRI Images e) Cluster 4 Figure 4: Cluster formed from c-means algorithm 5.4 IMAGE CLASSIFICATION After proper feature extraction the database images are then classified by using neural network. These feature vectors are considered as neurons in ANN. A supervised algorithm called Back Propagation Neural Network is employed to identify and classify the images into various grades. A back propagation neural network is mainly used in conjunction with a gradient descent approach. In this method the input will propagate layer by layer. When the input data is propagated in a forward direction a weight updation is generated in the backward direction. Hence we can state that the algorithm is basically a two phase cycle. When the input reaches the output stage a comparative analysis is done with the expected output and an error value is generated. This error value is back propagated to the previous nodes such that each node will have an associated error value which roughly represents its contribution to the original output. These error values are used to update the weights so as to reduce the loss function. Hence as the network gets trained, the neurons in the intermediate layers organize themselves in such a manner that the different neurons learn to recognize different characteristics of the total input space. The basic flow diagram of back propagation algorithm is given in figure 5. The basic algorithm of a BPNN is as follows Figure 5 Basic flow diagram of BPNN Initialize the connection weights into small random value. The input and target data are given to the network. The input value is iterated and the passed from lower layer to higher layer. At every neuron in the layer obtain the output and error value. Calculate error value for every neuron in every layer in backward order from output to input and change the weights according to the loss function. Repeat step ii to step v until the root mean square of output error is minimized editor@iaeme.com

8 Prajith Prakash Nair and T.R.Gopalakrishnan Nair The figure 6 denotes the confusion matrix of the neural network along with the ROC. Figure 6 Region of Convergence Plot of BPNN 6. CONCLUSION The accurate detection and classification of the prostate cancer disease is important for the successful treatment of the disease. This paper discusses a novel technique of employing back propagation network for the classification of prostate MRI images according to Jewett- Whitmore classification. A feature extraction algorithm is employed to extract the morphological features. The extracted features are used for segmentation purposes. The use of Artificial Neural Network methods is employed for classification of the cancer images. From the above method we have obtained a classification accuracy of 96.77%. REFERENCES [1] R. J. Cohen, B. A. Shannon, M. Phillips, R. E. Moorin, T. M. Wheeler, and K. L. Garrett, Central zone carcinoma of the prostate gland: a distinct tumor type with poor prognostic features, The Journal of Urology, vol. 179, no. 5, pp , 2008 [2] A. Sciarra, G. Mariotti, S. Salciccia et al., Prostate growth and inflammation, Journal of Steroid Biochemistry and Molecular Biology, vol. 108, no. 3 5, pp , 2008 [3] Anant Madabhushi,Michael D Feldman, Scott Doyle, Automated grading Prostate Cancer using Architectural and Textural Image Features,Proc. of IEEE international Symposium on Biomedical Imaging, pp ,ISBN [4] W. Allsbrook, K.A. Mangold, et al., Interobserver reproducibility of gleason grading of prostatic carcinoma: General pathologist, Hum. Path., vol. 32, no. 1, pp , [5] Doyle, et al., Automated grading of prostate cancer using architectural and textural image features, IEEEISBI, [6] S. Doyle, A. Madabhushi, et al., A boosting cascade for automated detection of prostate cancer from digitized histology, MICCAI, vol. 4191, pp , [7] Rahmadwati, Golshah Naghdy, Monserrat,Catherine Todd, Eviana Norahmawati, "Cervical Cancer classification using Gabor Filters," Proc. International Conference on healthcare Informatics, Imaging and system biology, vol. 1, pp.48-52, [8] Z. Yufeng Breast cancer detection with gabor features from digital mammograms Algorithms, 3(19):44{62, January editor@iaeme.com

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