Available online at ScienceDirect. Procedia Computer Science 102 (2016 ) Kamil Dimililer a *, Ahmet lhan b
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1 Available online at ScienceDirect Procedia Computer Science 0 (06 ) th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 06, 9-30 August 06, Vienna, Austria Effect of image enhancement on MRI brain images with neural networks Kamil Dimililer a *, Ahmet lhan b a,* Department of Electrical and Electronic Engineering, Near East University, P.O.Box:9938, Nicosia, North Cyprus, Mersin 0 Turkey b Computer Engineering Department, Near East University, P.O.Box:9938, Nicosia, North Cyprus, Mersin 0 Turkey Abstract Since human body s standard control metabolism stops, old cells do not die and these abnormal cells forms a mass of tissue, known as tumor. In order to diagnose tumors, huge biomedical machines such as MRI, PET-CT, and MG machines are used. Last decade, there are many studies in brain tumor detection in magnetic resonance imaging (MRI). Location of the brain tumor and precise size are detected with brain tumor detection. In this paper, image processing techniques are applied on MRI images to preserve information details. These techniques are erosion, contrast enhancement and median filtering. The purpose of observe is develop an image processing algorithm for brain cancer detection on MRI images. Comparison of back propagation neural networks will be done using original images and reconstructed images on the effect of categorization. 06 The Authors. Published by by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer-review ( under responsibility of the Organizing Committee of ICAFS 06. Peer-review under responsibility of the Organizing Committee of ICAFS 06 Keywords: Contrast Enhacement; MRI; Brain Tumor; Artificial Neural Networks. Introduction In literature, brain tumors are defined as malignant cells rise up in the brain. These cancerous cells grow into a mass of cancer tissues that interferes with brain capabilities consisting of muscle control, sensation, memory, and different everyday body capabilities. As stated in WHO's report in early 000s, fatal injuries by cancer is 6. million in worldwide. Most cancerous cells are referred to as malignant tumors, and people composed of specifically non-cancerous cells are called benign tumors. There are two types of brain tumors named as primary and secondary. Cancer cells that build from brain tissue are known as primary brain tumors, whereas tumors that spread from different organs to the brain are grouped as secondary brain, 3. tumors or metastatic. Brain tumors are categorized as a four malignant stages which is called Grade I, II, III and IV Benign brain tumor that consist cancer cells, can be removed. Benign brain tumors usually have an explicit side or edge. They do not expand to other parts of the body. However, benign tumors can induce serious health problems. Grade I and II are benign brain tumors 3, 4, 5. The other type of tumors, which is called as malignant brain tumor consists of cancer cells and it is also called as brain cancer. They are likely to grown up swiftly and can affect nearby normal brain tissues. This type of tumors can be a threat for life. Malignant brain tumors are in grade III and IV 3, 4, 5. In diagnosis and early detection of tumors, medical imaging plays an important role. Magnetic resonance imaging (MRI), computed tomography (CT), and different imaging techniques provide an efficient way to detect diseases 6. MRI uses a magnetic subject and pulses of radio waves to make images of organs and systems within the body. In many cases, MRI offers different * Corresponding author. Tel.: +90 (39) / 380; fax: +09 (39) address: kamil.dimililer@neu.edu.tr The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the Organizing Committee of ICAFS 06 doi:0.06/j.procs
2 40 Kamil Dimililer and Ahmet İlhan / Procedia Computer Science 0 ( 06 ) facts about systems in the body than may be visible with a MG, or CT scan. MRI may also display problems that cannot be visible with different imaging methods 6, 7. Researchers are using multi-disciplinary approach comprising knowledge in medicine, mathematics and computer science to get better understanding the disease and applied treatment way effectively. Since brain images are complicated and tumors can be analysed only by expert physicians, medical image processing for brain tumor detection is one of the challenging tasks in computer science. Various applications include digital image processing such as detection of criminal face, figure print authentication system, object recognition etc. Brain tumor detection which is an application of medical imaging plays an important role. Brain tumor detection depends on the affected part of the brain along with its shape, size, and boundary 4. For such duties, image processing techniques were used with the aid of artificial neural networks. Back propagation is a supervised system and popular in learning techniques with the multi-layer network. In back propagation neural networks, process of information flows from the direction of the input layer towards output. The learning is achieved by adjusting the connection weight in back propagation neural networks iteratively so that trained. Iteration numbers of the training algorithm and the convergence time will vary depending on diagnosis problem with their various data set 5. In this research two phases are designed. These phases are called Image with Neural Networks (IWNN), and Image Processing with Neural Networks (IPWNN). The aim of this paper is to perform image processing techniques, as well as artificial neural networks and their interrelated analysis methods to benign brain tumors patients. Ultimate research relay on quantitative information for instance, the shape, size and the ratio of the affected cells 7, 8.. Systems In this paper, an Image with Neural Networks (IWNN), and Image Processing with Neural Networks (IPWNN) are designed. The suggested system is an image processing model that combines different techniques in the field of image processing 0. The goal of this system is to detect brain tumors located within the MRI image. Matlab programming language is used for implementing and simulating the suggested system (Matlab 03a). Initially, morphological operations have been applied to the input image. Afterwards, the original input image is firstly processed by morphological operations and then with image erosion technique. Contrast enhancement and median filtering are applied to the processed image respectively. The suggested system will be implemented using 50 images that are randomly selected within the medical image database. The images are of size 56*56 raw type images. 3. Proposed Systems This section focusses on utilities and parameters of the proposed system. IWNN is a backpropagation neural network which uses the original input brain image set, whereas IPWNN is a backpropagation neural network system that uses image processing techniques applied to the original images. 3.. IWNN: Image with Neural Networks In this system, two phases are designed. First phase is preparation of the input image for the neural network phase which is considered as second phase. Figure represents the sample images from the database. (a) (b) Fig.. Original Sample images
3 Kamil Dimililer and Ahmet İlhan / Procedia Computer Science 0 ( 06 ) Data Preparation Phase Initially, the original image has been converted to grayscale in order to minimize the CPU time. The grey color image contain of pixel intensity between 0-55 where 0 represents black and 55 is for white 0. Original image sizes are 56 x 56, whereas 64 x 64 images are preferred considering the processing time. These two resizing can be found in the figure. (a) (b) Fig.. (a) 56x56 Original Image; (b) 64x64 Resized Image 3... Neural Networks Phase IWNN uses a conventional 3-layer back propagation neural network with 4096 input neurons, 35 hidden neurons and output neurons classifying the organs with tumors and without tumors. Output neurons classify the organs using binary coding: [ 0] for the Organ with Tumor and [0 ] for the Organ without Tumor. The sigmoid activation function is used for activating neurons in the hidden and the output layers. Figure 3 represents the Neural Network topology and Table represents the final parameters of IWNN. IWNN Original Image (56x56) pixels Reduced Size Image (64x64) pixels Received Image Fig. 3. Neural Network Topology of IWNN. Table : Trained neural network final parameters IWNN Input Layer Nodes 4096 Hidden Layer Nodes 35 Output Layer Nodes Learning Rate Momentum Rate 0.5 Minimum Error 0.00 Iterations 73 Training Time 36 seconds
4 4 Kamil Dimililer and Ahmet İlhan / Procedia Computer Science 0 ( 06 ) IPWNN: Image Processing with Neural Networks IPWNN uses a conventional 3-layer back propagation neural network with 4096 input neurons, 6 hidden neurons and output neurons classifying the organs with tumors and without tumors. Output neurons classify the organs using binary coding: [ 0] for the Organ with Tumor and [0 ] for the Organ without Tumor. The sigmoid activation function is used for activating neurons in the hidden and the output layers. Figure 4 represents the Neural Network topology and Table represents the final parameters of IPWNN. IPWN N Original Image (56x56) pixels Reduced Size & Adjusted Image (64x64) pixels Fig. 4. Neural Network Topology of IPWNN. Received Image Table : Trained neural network final parameters IPWNN Input Layer Nodes 4096 Hidden Layer Nodes 6 Output Layer Nodes Learning Rate Momentum Rate 0.6 Minimum Error 0.00 Iterations 3676 Training Time 45 seconds 4. Discussions Figure 3 and figure 4 shows the topologies of the suggested BPNN Systems. Due to the implementation simplicity, and the availability of sufficient input target database for training, the use of a back propagation neural network which is a supervised learner, has been preferred. Training and testing (generalization) are comprised for these phases. The available brain image database is organized as follows:. Training image set: 0 images. Testing image set: 30 images During the learning phase, initial random weights of values between -0.4 and 0.4 were used. In order to achieve the required minimum error value and meaningful learning, the learning rate and the momentum rate were adjusted during various experiments. An error value of 0.00 was considered as sufficient for this application. 5. Results These results were obtained using a 0R 64 bits OS Virtual machine that has a Gbits connection speed to a super computer that has IBM Blade HS XM Intel Xeon.33 GHz CPU with 80 core units. Through this application, the robustness, flexibility and speed of this intelligent brain tumor detection system have been demonstrated. Tumor identification results using the training image set yielded 00% recognition as would be expected in both BPNN. The testing image sets of IWNN and IPWNN results were successful and encouraging. An overall correct identification of IWNN yielded 83% correct identification where, 5 images out of the available 30 brain images yielded. An overall correct
5 Kamil Dimililer and Ahmet İlhan / Procedia Computer Science 0 ( 06 ) identification of IPWNN yielded 90% correct identification where, 7 images out of the available 30 brain images yielded. This successful result was obtained by using only the database of images for training the neural network. (a) (b) Fig.5. (a) IWNN RMS Error Graph; (b) IPWNN RMS Error Graph
6 44 Kamil Dimililer and Ahmet İlhan / Procedia Computer Science 0 ( 06 ) As it can be seen from the results of the iterations using the Super computer, IWNN finalized the training after 73 iterations within 36 seconds, whereas IPWNN finalized the training process after 3676 iterations within 45 seconds. Additionally, hidden layer nodes, learning rates, and momentum rates for both IWNN and IPWNN have been obtained after several experiments. Figure 5 (a) and Figure 5 (b) shows the RMS Error graphs for the two neural networks. IPWNN results using the testing image sets were successful and encouraging. An overall correct identification of IPWNN yielded 90% correct identification where, 7 images out of the available 30 brain images yielded. These successful results were obtained by using only the database of images for training the neural network. 6. Conclusions In this paper, two new approaches for the detection of brain tumors were developed. These new approaches are based on image processing techniques as well as neural networks. The used image processing techniques are very useful and significant in the medical field such as in the MRI machine. The robustness in the developed system is to stimulate the human visual inspection that usually highlights or notice the presence of any abnormality in the brain directly by the first look. Likewise, the system marks the abnormalities or tumors in an iris directly in 0.0 seconds which makes our system robust and effective. The images used in testing the proposed system are collected from two databases to get a total of 50 images: 5 normal and 5 abnormal. The experimental results obtained when testing the proposed system proved that the developed brain tumor detection system is a robust image processing system that is capable of detecting any abnormalities in the brain region particularly at small patches. References. Noureen E, Hassan K. Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point. IOSR Journal of Electrical and Electronics Engineering 04; 9(5): Masalkar D N, Shitole A S. Advance Method for Brain Tumor Classification. International Journal on Recent and Innovation Trends in Computing and Communication 04; (5): Sharma Y, Chhabra M. An Improved Automatic Brain Tumor Detection System. International Journal of Advanced Research in Computer Science and Software Engineering 05; 5(4): Mansa S M, Kulkarni N J, Randive S N. Review on Brain Tumor Detection and Segmentation Techniques. International Journal of Computer Applications 04; 95: Anandgaonkar G, Sable G. Brain Tumor Detection and Identification from T Post Contrast MR Images Using Cluster Based Segmentation, International Journal of Science and Research 04; 3(4): Ravi A, Sreejith S. A Review on Brain Tumor Detection Using Image Segmentation. International Journal of Emerging Technology and Advanced Engineering 05; 5(6): Mustaqeem A, Javed A, Fatima T. An Efficient Brain Tumor Detection Algorithm Using Watershed and Thresholding Based Segmentation. I. J. Image Graphics and Signal Processing 0; 0(5): Saini P K, Singh M. BRAIN Tumor Detection in Medical Imaging Using Matlab. International Journal of Engineering and Technology 05; (): Swathi P S, Devassy D, Vince P, Sankaranarayanan P N. Brain Tumor Detection and Classification Using Histogram Thresholding and ANN. International Journal of Computer Science and Information Technologies 05; 6(): Ghare S, Gaikwad N, Kulkarni N, Nerkar M. Detection of Possibility of Brain Tumor Using Image Segmentation. International Journal of Innovative Research in Computer and Communication Engineering 05; 3(4): Joseph R P, Singh C S, Manikandan M. Brain Tumor MRI Image Segmentation and Detection in Image Processing. International Journal of Research in Engineering and Technology 04; 3(): -5.. Sachin N, Shah D, Khairnar V, Kadu S. Brain Tumor Detection Based on Bilateral Symmetry Information. International Journal of Engineering Research and Applications 04; 4(6): Sravanthi K, Samiuddin S. Brain Tumor Detection, Demarcation and Quantification via MRI. International Journal & Magazine of Engineering Technology, Management and Research 05; (8): Selkar R G, Thakare M N. Brain Tumor Detection and Segmentation by Using Thresholding and Watershed Algorithm. International Journal of Advanced Information and Communication Technology 04; (3): Vally D, Sarma Ch V. Diagnosis Chest Diseases Using Neural Network and Genetic Hybrid Algorithm. International Journal of Engineering Research and Applications 05; 5(): Logeswari T, Karnan M. An improved implementation of brain tumor detection using segmentation based on soft computing. Journal of Cancer Research and Experimental Oncology 00; (): Dimililer K. Neural Network Implementation for Image Compression of X-Rays. Electronics World 0; 8(9): Dimililer K. Back Propagation Neural Network Implementation for Medical Image Compression.Journal of Applied Mathematics 03;453098: Khashman A, Dimililer K. Comparison Criteria for Optimum Image Compression. The International Conference on Computer as a Tool (EUROCON 05), Serbia & Montenegro, -4 November Dimililer K, Kirsal-Ever Y, Ugur B. Tumor Detection on CT Lung Images using Image Enhancement. International Science and Technology Conference (ISTEC 06), Austria, 3-5 July 06.
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