BRAIN TUMOR SEGMENTATION USING DEEP NEURAL NETWORK

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1 Volume 120 No , ISSN: (on-line version) url: BRAIN TUMOR SEGMENTATION USING DEEP NEURAL NETWORK Shaik Nasir 1, D.Pradeep Kumar Reddy 2, J.Mohana 3, 1,2 B.E(ECE), Saveetha School of Engineering, Saveetha University, 3 Associate Professor, ECE, Saveetha School of Engineering, Saveetha University. 1 shaiknasir1995@gmail.com, 2 pradeepreddy9168@gmail.com, 3 mohana@saveetha.com July 23, 2018 Abstract Brain MR image segmentation is a very important and challenging task that is needed for the purpose of diagnosing brain tumors and other neurological diseases. Brain tumors have different characteristics such as size, shape, location and image intensities. They may deform neighboring structures and if there is edema with the tumor, intensity properties of the nearby region change. Deep Neural Networks (DNNs) have recently attracted more attention due to their state-of-the-art performance on several datasets. DNNs have also been applied successfully to segmentation problems using DNNs in order to find the brain tumor. Deep Neural Networks (DNNs) are often successful in problems needing to extract information from complex, high-dimensional inputs, for which useful features are not obvious to design. To apply DNNs to brain tumor segmentation for the BRATS challenge

2 1 Introduction Brain tumors can be either malignant (cancerous) or benign (noncancerous). Primary brain tumors (i.e., brain cancer) comprise two main types: gliomas and malignant meningiomas. Gliomas are a familiar type of malignant tumors that consist of a variety of types, named for the cells from which they occur: astrocytomas, oligodendrogliomas, and ependymomas. Meningiomas arise from the meninges, which are tissues that surround the external part of the spinal cord and brain. The majority of meningiomas are benign and can be cured by surgery.there are a number of extraordinary brain. tumors, with medulloblastomas, which develop from the primitive stem cells of the cerebellum and are most often seen in children. The brain is a site where both primary and secondary malignant tumors can occur; secondary brain tumors usually begin to another place in the body and next metastasize, or spread, to the brain. The causes of brain tumors are unknown, a small number of risk factors have been proposed. These include head injuries, hereditary syndromes, immune suppression, prolonged exposure to ionizing radiation, electromagnetic fields, cell phones, or chemicals like formaldehyde and vinyl chloride. Symptoms of brain tumors include persistent headache, nausea and vomiting, eyesight, hearing and/or speech problems, walking and/or balance difficulties, personality changes, memory lapses, problems with cognition and concentration, and seizures. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, cellular structure and vascular supply, making it an important tool for the valuable diagnosis, treatment and monitoring of the disease. Meningioma is a variety of tumor that develops from the meninges. The dura mater, arachnoid and Pia mater are the layers of meanings. Meningiomas are categorized as benign tumors, with the 10% being atypical or malignant. Benign meningiomas grow gradually that depends on where it is located, a meningioma achieve a relatively large size before it causes symptoms. Other meningiomas grow more rapidly, or have sudden growth spurts. There is no way to calculate the growth for a meningioma. Glioma is a tumor that starts within the brain or spine. It is called glioma since it arises from glial cells. The most common position of gliomas is the brain. In todays digital era, capturing, storing and analysis of

3 medical image had been digitized. Even with state of the art techniques, detailed interpretation of medical image is a challenge from the perspective of time and accuracy. The challenge stands tall especially in regions with abnormal Color and Shape which needs to be identified by radiologists for future studies. The key ask in designing such image processing and a computer vision application is the accurate segmentation of medical images. This century will pass away, but the birth of medical computing and its reward to advances in medicine will usher in a new plate of technological innovations with a focus on ideal and convenient delivery of medical services. Both medicine and computing are growing at a rapid rate. Undoubtedly the growth in medicine has benefited much from the growth in computers. Precise, diagnosis, fast data and voice communication. The late 1960s when Sir Godfrey Hounsfield, from the United Kingdom, created the first commercially feasible CT Scanner. Now, Scientists and Researchers are used the MRI and CT Scans are used in the field of identifying the internal parts of the human body, especially for Brain Tumors (BT). 2 Literature Survey A lot of work has been proposed by researchers for the MRI brain image segmentation and tumour detection technique. A short review of some recent research work is presented here. According to the literature study, B. Kekre et al [2] have presented a quantization segmentation method to detect cancerous mass from MRI images. In order to increase radiologist s diagnostic performance, computeraided diagnosis scheme have been developed to improve the detection of primary signatures of this disease: masses and micro classification. Morphological segmentation extracts other regions with tumour region. Thresholding is used to convert input image into binary image. Global threshold methods suffer from drawback as threshold value was given manually. The algorithms were tested on twenty one MRI images. Identification rate for Morphological Segmentation was 66.7%.S. Klein, et al [4] studied the likelihood in a premature period of detecting dementia, using no rigid registration of MRI. A k-nn classifier was train on the dissimilarity matrix and the performance is tested in a leave-one-out experiment on

4 images. A. El-Dahshan, T.Hosny, and A.M. Salem [5], presented proposed hybrid techniques consist from three steps, extraction of feature using DWT, reduce the large dimension using principal component analysis PCA and classify the output using two classifiers. The first classifier based on ANN and the other classifier is based on k- nearest neighbour (k-nn). S. Chaplot, L. Patnaik, and N. Jagannathan, [6], the authors used ANN and SVM to classify brain MRI. The pre-processing phase uses DWT and used as input for Neural Network NN and SVM. 3 Proposed System In proposed technique, firstly the enter MRI picture is pre-processed to remove the noise and make the picture noise loose for the following method. figure 1 shows the block diagram of proposed machine which consist seven blocks The Gaussian clear out and RGB to gray photograph converter have been used inside the preprocessing stage. finally, the pre- processed image is segmented the usage of the changed place developing and everyday area developing technique. In modified area growing the orientation constraints similarly to the normal intensity constraints can be considered. Figure 1. Block Diagram of Proposed System

5 3.1 Input MRI Brain Images Data The MRI photo dataset which have utilized in photo segmentation approach is to be had from the publicly available resources. This dataset carries mind MRI photographs with tumour and without tumour. The discern 2 indicates the sample MRI photos with tumour and non-tumour pictures. Figure 2. MRI image dataset, MRI Normal Images 3.2 Pre-processing MRI brain photographs cannot be fed immediately because the enter for the proposed technique. The input picture is subjected to a set of preprocessing steps so that the image receives converted appropriate for in addition processing. The parent 2 indicates input MRI image with tumour. There two step preprocessing manner wherein first the enter image passing thru the Gaussian filter out which complements the image first-class. within the second step inside the pre-processing, the photo is converted from the RGB model to gray image which makes the photograph healthy for region growing system that is shown in determine 3 and figure four. whilst operating with photos, it s far vital to apply the two dimensional Gaussian function. that is surely the fabricated from 1D

6 Figure 3. Input MRI image Figure 4. Preprocessed MRI image 3.3 Modified Region Growing Technique Gaussian functions (one for each direction) and is given via: (1) discern three: input MRI photo parent four: Preprocessed MRI image 3.3 modified location developing approach place growing is a simple image segmentation approach based on the region. This approach to segmentation is to check the neighboring pixels of initial seed points and checks whether the pixel buddies should be brought to the place or not, primarily based on certain conditions. The manner is iterated to yield exclusive regions. In a ordinary place developing method, the neighbor pixels are tested by way of best the depth constrain. For this, a threshold degree for depth fee is ready and people neighbor pixels that satisfy this threshold is chosen for region growing. The regular place growing has the downside that noise or variation of intensity may bring about holes or over segmentation. another drawback is that the approach won t distinguish the shading of the real snap shots. For enhancing the

7 ordinary region growing and correctly tackling the draw backs of a everyday place which shown in the figure 5. Figure 5. Gridded MRI Image Location developing is a simple image segmentation approach based totally on the area. This technique to segmentation is to check the neighboring pixels of preliminary seed points and tests whether the pixel buddies have to be delivered to the region or no longer, primarily based on certain conditions. The procedure is iterated to yield special areas. In a everyday area developing method, the neighbor pixels are examined by most effective the depth constrain. For this, a threshold stage for intensity price is ready and the ones neighbor pixels that satisfy this threshold is chosen for area developing. The everyday area growing has the disadvantage that noise or version of intensity may additionally bring about holes or over segmentation. some other disadvantage is that the technique may not distinguish the shading of the actual snap shots. For enhancing the normal region growing and correctly tackling the draw backs of a ordinary region. growing, they delivered an additional constrain of orientation. in the modified region developing, there are thresholds; one is for the intensity and the other for orientation. region is grown if best each constrains are met. For evaluation of the propose technique, spilt the unique photograph into four, 18 and 24 grids. Gridding results in smaller grids in order that analysis can be carried out effortlessly that s shown in figure 5.in this technique each of the grids is treated one at a time to which the vicinity growing method is implemented. The preliminary step in area growing for the grid shaped is to select a seed factor for the grid. The initial region starts with the exact place of the seed

8 also to discover the seed factor of the grid histogram evaluation is achieved. The histogram is observed out for every pixel in the grid. Because the picture is a gray scale photograph, the values of this photograph is from zero to 255. For every grid, the histogram fee that comes most common is selected because the seed point pixel. From this, someone of the seed point pixel is taken because the seed factor for the grid. After finding out the seed point, the region is grown from it. The neighboring pixels are in comparison with the seed factor and if the neighbor pixel satisfies constrains, then the area is grown else it isn t always grown to that pixel. parent 6 suggests ordinary place growing Segmented photograph. Figure 6. Segmentation with Normal Region Growing method 3.4 Final Classification In-order to detect the presence of the tumour inside the input MRI image, in this approach use the neural network classifier to categorise the picture into tumourous or not and its disorder type additionally. The neural network consists of three layers which can be enter layer output layer. first of all the neural networks are educated by the features which might be extracted inside the previous step. For the schooling cause, we have used about

9 Figure 7. Segmentation with Proposed Modified Region growing method MRI snap shots of which 15 are normal and the alternative 15 are tumourous. The neural community is skilled with functions of those snap shots. we ve applied Feed ahead Neural community (FFNN) and Radial basis characteristic (RBF) neural network for comparative evaluation. The enter MRI photo is fed into the trained neural network after the pre-processing and changed location growing. The classifier compares the educated information with those of the enter picture feature records and classifies it into tumourous or regular. 4 Conclusion The method includes pre-processing, segmentation; function extraction of the place and final type. The normal area developing has the drawback of noise or variant of intensity which may additionally bring about over segmentation. to overcome this downside a further constrains of orientation is brought inside the changed location growing. with the aid of analyzing the effects, the performance of the proposed approach has drastically advanced the tumour detection as compared with the vicinity growing algorithm based MRI segmentation. References [1] A.R.Kavitha,Ms.Kavin Rupa, Dr.C.Chellamuthu An Efficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRI Images,

10 [2] H. B. Kekre, Kavita Raut, Tanuja Sarode, Detection of Tumor inmri Using Vector Quantization Segmentation, International Journal of Engineering Science and Technology, Vol. 2, No: 8, pp: , [3] Jue Wu, Albert C.S. Chung, A novel framework for segmentation of deep brain structures based on the Markov dependence tree, Neuro Image, Elsevier, vol: 46, pp: , [4] S. Klein, et al. Early diagnoses of dementia based on inter subject whole- brain dissimilarities, Proc.IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE Press April 2010,pp , doi: /isbi [5] E. A. El-Dahshan, T. Hosny, and A. M. Salem, Hybrid intelligent techniques for MRI brain images classification, Journal of Digital Signal Processing, vol. 20(2), March 2010, pp ,doi: /j.dsp [6] S.Chaplot, L. Patnaik, and N. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control, vol. 1(1), 2006, pp

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