Development of Novel Approach for Classification and Detection of Brain Tumor

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1 International Journal of Latest Technology in Engineering & Management (IJLTEM) ISSN: Development of Novel Approach for Classification and Detection of Brain Tumor Abstract This paper proposed the detection of brain tumor from MRI images. The methodology consists of three steps: segmentation, decomposition and classification. Brain tumor removal and its examination are difficult tasks in medical image processing because brain image and its structure is problematical that can be analyzed only by expert radiologists. In this paper, tumor region detected in the brain using MRI images by a computer-based method. A classification of brain into healthy brain or a brain having a tumor is first done which is then followed by further classification into benign or malignant tumor. An enhancement process is applied to improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase. Then we apply Wavelet Transform in the segmentation process to decompose MRI images. A user friendly Matlab program has been constructed to test the proposed algorithm. A wavelet approach for brain tumor detection and classification through magnetic resonance images has been proposed. Keywords: Cerebral MRI images, Wavelet Transform, tumor, feature extraction, segmentation, and decomposition. INTRODUCTION Patients affected by brain tumors needs follow up and planned treatment which solely depends upon proper diagnosis. The anatomical structure and potential abnormal tissues are diagnosed based on MRI images and to some extent pathological reports. Despite numerous efforts and promising results in the medical imaging community, accurate and reproducible segmentation and characterization of abnormalities are still a challenging and difficult task because of the variety of the possible shapes, locations and image intensities of various types of tumors [1]. Some of them may also deform the surrounding structures or may be associated to edema or necrosis that changes the image intensity around the tumor. Existing methods leave significant room for inc reased automation, applicability and accuracy. This paper introduces a novel method to segment the affected mass in a brain MRI images. Researches carried till now in this area are studied before introducing our effective method. In [2] the authors proposed hybridized multilevel thresholding and level set method for automatic segmentation of brain tumor. Their novel technique is to interface the initial segmentation from multilevel thresholding and extract a fine portrait using level set method with morphological operations. In [3] the research work proposed fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. In [4] they proposed an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 3 kernels. They also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods for brain tumor segmentation. A generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter was proposed in [5]. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion app earance across modalities, an important feature of many brain tumor imaging sequences. They also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as tumor core or fluid-filled structure. The study in [6] proposed an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. The swarm intelligence algorithm was applied on sample CT images and X-rays whose status have been determined by senior radiologists. Mass of unwanted cell growth in the brain leads to brain tumor. The tumors are classified as 1) Non-cancerous (Benign) and 2) cancerous (Malignant) tumors. The non cancerous tumor can either originate in the brain itself and stay the brain (primary brain tumor) or cancers that have spread to the brain tissue from tissue elsewhere in the Volume 2 Issue 2 page 6

2 body(secondary brain tumor ) Benign brain tumors do not contain cancer cells it can be removed and rarely grow Magnetic resonance imaging (MRI) is considered now as an important tool for surgeons. Age is not a factor in brain tumors, generally it is more common in older people. Approximately 7, new cases of primary brain tumors around the world have been diagnosed in 214. More than 4,6 children between the ages of 9 have been diagnosed with brain tumor in 214. Brain tumors establish for the second leading cause of cancer-related deaths in children under age 2 and in males aged Cancerous brain tumors are the second most common type of childhood cancer after leukemia. The tumor is a growth of abnormal cells in the tissues of the brain.brain tumors are detected by different techniques listed below, I. MRI Scan (Magnetic Resonance Imaging) 2. CT Scan (Computed Tomography) 3. PET Scan (Positron Emission Tomography) METHODOLOGY We had proposed a novel method to detect brain tumor region of interest using wavelet based histogram thresholding the method goes through two stage segmentation that are coarse and fine two level wavelet decomposition is applied and corresponding histograms are thresholded for all wavelet components. The thresholds are adaptive. Finally window based segmentation is applied to remove false segmented areas using coarse segmentation. 41 images were listed out of which 15 are normal and 26 are tumor affected. The proposed technique shows 1% accuracy. Mother wavelet is a wave like oscillation with amplitude that begins at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" like one recorded by heart monitor. Generally, wavelets have specific properties that make them useful for signal processing. We had used two dimensional wavelet with mother wavelet being db6. Fig. 1 Wavelet Decomposition at 2 level Convert the MRI image to gray scale two dimensional in the range of [-255] and find the histogram. Volume 2 Issue 2 page 7

3 The Original image The Gray Scale image The Contrast stretched image 2 x 14 The Histogram Fig. 2 Original image, Gray scale image, illumination corrected image and its histogram In coarse segmentation, we applied mother wavelet on 2 dimensional image and decomposed it. High frequency components are removed using wavelet histograms. All components low-low, low-high, high-low, high-high are adaptively thresholded. The Histogram-scale 1 Approx 6 The Histogram-scale 2 Approx The Histogram-scale 3 Approx The Histogram-scale 4 Approx Fig. 3 Low-low approximation Volume 2 Issue 2 page 8

4 2 x 14 The Histogram-Horizontal1 5 The Histogram x 14 The Histogram The Histogram Fig. 4 Horizontal component 2 x 14 The Histogram-Vertical1 5 The Histogram x 14 The Histogram x 14 The Histogram Fig. 5 Vertical component Low frequency components of 2dimentional image are further applied mother wavelet Volume 2 Issue 2 page 9

5 2 x 14 The Histogram - Diagonal1 1 1 x 14 The Histogram x x The Histogram x 14 The Histogram Fig. 6 Low-low approximation 2 2 The Histogram-Horizontal2 1 The Histogram The Histogram The Histogram Fig. 7 Low-high horizontal 2 Volume 2 Issue 2 page 1

6 2 The Histogram-Vertical2 5 The Histogram The Histogram The Histogram Fig. 8 High-low vertical 2 2 The Histogram - Diagonal2 1 The Histogram The Histogram The Histogram Fig. 9 High-high component 2 We found the histogram for all 8 components of wavelet coefficient and the minimum value has been added in wavelet coefficient so as to minimum value become zero. Now we found the maximum value and divided all values by this maximum value. Next, multiply 255 to all values.we got the Fig.ure which contains value between [-255]. Next, we found the histogram for all 8 components and apply 1 dimensional wavelet decomposition for all histogram upto 5 level. At 5 th level of decomposition we got the threshold for 6 component of each individual component. Then we considered low-high component to threshold 1.value of component is less than threshold it consider to be zero and if it is greater and equal to then it takes its original value. then we reconstruct the Fig.ure by applying inverse wavelet. We got low-low approximation 1 then again we apply the inverse wavelet to get real image. By the help of wavelet segmentation we got the image which has value between [-255]. Volume 2 Issue 2 page 11

7 Fig. 1 Wavelet reconstruction after adaptive thresholding GLOBAL THRESHOLDING In global thresholding, threshold we find the mean of all thresholding and it apply to the low frequency component if the value is greater than threshold then it consider to be 1 else. Now our image is in binary form. II] In fine segmentation, we have taken the widow size to be 15. Then we multiply 15*15 to get Wsum. The threshold is calculated as, T=Wsum/2; Now we applied the padded window to binary image. Each window is thresholded using the above threshold value. Fig. 11 fine segmented RESULT & CONCLUSIONS In this work, we detected normal and abnormal images using image segmentation is presented based on adaptive thresholds through coarse and fine segmentation. The MRI Images were collected from brain atlas website, image size were 256*256 of JPEG format. It is an efficient method of detecting MRI of brain into normal and abnormal which is cancerous or noncancerous. The proposed Volume 2 Issue 2 page 12

8 approached provide very promising results with 1% accuracy. As far as the region of interest is concerned, the affected region so segmented was approximately equal to the ground truth. This system is efficient since adaptive thresholding is used. The thresholds are selected depending upon the image characteristics regarding illumination, noise, bins etc. REFERENCES [1] A.W. Toga, P.M. Thompson, M.S. Mega, K.L. Narr, R.E. Blanton, Probabilistic approaches for atlasing normal and disease-specific brain variability, Anatomy and Embryology 24 (4) (21) [2] Malsawm Dawngliana, Daizy Deb, Mousum Handique, Sudipta Roy, Automatic brain tumor segmentation in MRI: Hybridized multilevel thresholding and level set, IEEE International Symposium on Advanced Computing and Communication (ISACC), pp , September 215. [3] Yehualashet Megersa, Getachew Alemu, Brain tumor detection and segmentation using hybrid intelligent algorithm, IEEE conference, AFRICON, 215, pp. 1-8, September 215. [4] Sergio Pereira, Adriano Pinto, Victor Alves, Carlos A. Silva, Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks, IEEE Transactions on Medical Imaging, Volume:35, Issue: 5, pp , May 216. [5] Bjoern H. Menze, Koen Van Leemput, Danial Lashkari, Tammy Riklin-Raviv, Ezequiel Geremia, Esther Alberts, Philipp Gruber, Susanne Wegener, Marc-André Weber, Gabor Szekely, Nicholas Ayache, and Polina Golland, A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation With Application to Tumor and Stroke, IEEE Transactions on Medical Imaging, Volume: 35, Issue: 4, pp , 216. [6] Mohammad Majid al-rifaie, Ahmed Aber, Duraiswamy Jude Hemanth, Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation, IET Systems Biology, IEEE, Volume: 9, Issue: 6, pp , 215. Volume 2 Issue 2 page 13