brain tumor segmentation using deep neural network · brain tumor segmentation using deep neural...

BRAIN TUMOR SEGMENTATIONUSING DEEP NEURAL NETWORK

Shaik Nasir1, D.Pradeep Kumar Reddy2,J.Mohana3,

1,2B.E(ECE), Saveetha School of Engineering,Saveetha University,

3Associate Professor, ECE,Saveetha School of Engineering,

Saveetha [email protected],

[email protected],[email protected]

July 23, 2018

Abstract

Brain MR image segmentation is a very important andchallenging task that is needed for the purpose of diagnos-ing brain tumors and other neurological diseases. Braintumors have different characteristics such as size, shape,location and image intensities. They may deform neigh-boring structures and if there is edema with the tumor, in-tensity properties of the nearby region change. Deep Neu-ral Networks (DNNs) have recently attracted more atten-tion due to their state-of-the-art performance on severaldatasets. DNNs have also been applied successfully to seg-mentation problems using DNNs in order to find the braintumor. Deep Neural Networks (DNNs) are often successfulin problems needing to extract information from complex,high-dimensional inputs, for which useful features are notobvious to design. To apply DNNs to brain tumor segmen-tation for the BRATS challenge.

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 10121-10131ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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1 Introduction

Brain tumors can be either malignant (cancerous) or benign (non-cancerous). Primary brain tumors (i.e., brain cancer) comprise twomain types: gliomas and malignant meningiomas. Gliomas are afamiliar type of malignant tumors that consist of a variety of types,named for the cells from which they occur: astrocytomas, oligo-dendrogliomas, and ependymomas. Meningiomas arise from themeninges, which are tissues that surround the external part of thespinal cord and brain. The majority of meningiomas are benignand can be cured by surgery.There are a number of extraordinarybrain. tumors, with medulloblastomas, which develop from theprimitive stem cells of the cerebellum and are most often seen inchildren. The brain is a site where both primary and secondarymalignant tumors can occur; secondary brain tumors usually beginto another place in the body and next metastasize, or spread, tothe brain. The causes of brain tumors are unknown, a small num-ber of risk factors have been proposed. These include head injuries,hereditary syndromes, immune suppression, prolonged exposure toionizing radiation, electromagnetic fields, cell phones, or chemicalslike formaldehyde and vinyl chloride. Symptoms of brain tumorsinclude persistent headache, nausea and vomiting, eyesight, hear-ing and/or speech problems, walking and/or balance difficulties,personality changes, memory lapses, problems with cognition andconcentration, and seizures. Magnetic resonance imaging (MRI)provides detailed information about brain tumor anatomy, cellu-lar structure and vascular supply, making it an important tool forthe 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 mean-ings. Meningiomas are categorized as benign tumors, with the 10%being atypical or malignant. Benign meningiomas grow graduallythat depends on where it is located, a meningioma achieve a rel-atively large size before it causes symptoms. Other meningiomasgrow more rapidly, or have sudden growth spurts. There is noway to calculate the growth for a meningioma. Glioma is a tumorthat starts within the brain or spine. It is called glioma since itarises from glial cells. The most common position of gliomas isthe brain. In todays digital era, capturing, storing and analysis of

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medical image had been digitized. Even with state of the art tech-niques, detailed interpretation of medical image is a challenge fromthe perspective of time and accuracy. The challenge stands tallespecially in regions with abnormal Color and Shape which needsto be identified by radiologists for future studies. The key ask indesigning such image processing and a computer vision applicationis the accurate segmentation of medical images. This century willpass away, but the birth of medical computing and its reward toadvances in medicine will usher in a new plate of technological in-novations with a focus on ideal and convenient delivery of medicalservices. Both medicine and computing are growing at a rapid rate.Undoubtedly the growth in medicine has benefited much from thegrowth in computers. Precise, diagnosis, fast data and voice com-munication. The late 1960s when Sir Godfrey Hounsfield, from theUnited Kingdom, created the first commercially feasible CT Scan-ner. Now, Scientists and Researchers are used the MRI and CTScans are used in the field of identifying the internal parts of thehuman body, especially for Brain Tumors (BT).

2 Literature Survey

A lot of work has been proposed by researchers for the MRI brainimage segmentation and tumour detection technique. A short re-view of some recent research work is presented here. According tothe literature study, B. Kekre et al [2] have presented a quantizationsegmentation method to detect cancerous mass from MRI images.In order to increase radiologist’s diagnostic performance, computer-aided diagnosis scheme have been developed to improve the detec-tion of primary signatures of this disease: masses and micro clas-sification. Morphological segmentation extracts other regions withtumour region. Thresholding is used to convert input image intobinary image. Global threshold methods suffer from drawback asthreshold value was given manually. The algorithms were testedon twenty one MRI images. Identification rate for MorphologicalSegmentation was 66.7%.S. Klein, et al [4] studied the likelihood ina premature period of detecting dementia, using no rigid registra-tion of MRI. A k-NN classifier was train on the dissimilarity matrixand the performance is tested in a leave-one-out experiment on 58

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images. A. El-Dahshan, T.Hosny, and A.M. Salem [5], presentedproposed hybrid techniques consist from three steps, extraction offeature using DWT, reduce the large dimension using principal com-ponent analysis PCA and classify the output using two classifiers.The first classifier based on ANN and the other classifier is basedon k- nearest neighbour (k-NN). S. Chaplot, L. Patnaik, and N.Jagannathan, [6], the authors used ANN and SVM to classify brainMRI. The pre-processing phase uses DWT and used as input forNeural Network NN and SVM.

3 Proposed System

In proposed technique, firstly the enter MRI picture is pre-processedto remove the noise and make the picture noise loose for the follow-ing method. figure 1 shows the block diagram of proposed machinewhich consist seven blocks The Gaussian clear out and RGB togray photograph converter have been used inside the preprocessingstage. finally, the preprocessed image is segmented the usage of thechanged place developing and everyday area developing technique.In modified area growing the orientation constraints similarly tothe normal intensity constraints can be considered.

Figure 1. Block Diagram of Proposed System

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3.1 Input MRI Brain Images Data

The MRI photo dataset which have utilized in photo segmen-tation approach is to be had from the publicly available resources.This dataset carries mind MRI photographs with tumour and with-out tumour. The discern 2 indicates the sample MRI photos withtumour and non-tumour pictures.

Figure 2. MRI image dataset, MRI Normal Images

3.2 Pre-processing

MRI brain photographs cannot be fed immediately because theenter for the proposed technique. The input picture is subjectedto a set of preprocessing steps so that the image receives convertedappropriate for in addition processing. The parent 2 indicates inputMRI image with tumour. There two step preprocessing mannerwherein first the enter image passing thru the Gaussian filter outwhich complements the image first-class. within the second stepinside the pre-processing, the photo is converted from the RGBmodel to gray image which makes the photograph healthy for regiongrowing system that is shown in determine 3 and figure four. whilstoperating with photos, it’s far vital to apply the two dimensionalGaussian function. that is surely the fabricated from 1D

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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 MRIimage 3.3 modified location developing approach place growing isa simple image segmentation approach based on the region. Thisapproach to segmentation is to check the neighboring pixels of ini-tial ”seed points” and checks whether the pixel buddies should bebrought to the place or not, primarily based on certain conditions.The manner is iterated to yield exclusive regions. In a ordinaryplace developing method, the neighbor pixels are tested by way ofbest the ”depth” constrain. For this, a threshold degree for depthfee is ready and people neighbor pixels that satisfy this thresholdis chosen for region growing. The regular place growing has thedownside that noise or variation of intensity may bring about holesor over segmentation. another drawback is that the approach won’tdistinguish the shading of the real snap shots. For enhancing the

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ordinary region growing and correctly tackling the draw backs of aeveryday place which shown in the figure 5.

Figure 5. Gridded MRI Image

Location developing is a simple image segmentation approach basedtotally on the area. This technique to segmentation is to check theneighboring pixels of preliminary ”seed points” and tests whetherthe pixel buddies have to be delivered to the region or no longer,primarily based on certain conditions. The procedure is iteratedto yield special areas. In a everyday area developing method, theneighbor pixels are examined by most effective the ”depth” con-strain. For this, a threshold stage for intensity price is ready andthe ones neighbor pixels that satisfy this threshold is chosen for areadeveloping. The everyday area growing has the disadvantage thatnoise or version of intensity may additionally bring about holes orover segmentation. some other disadvantage is that the techniquemay not distinguish the shading of the actual snap shots. For en-hancing the normal region growing and correctly tackling the drawbacks of a ordinary region. growing, they delivered an additionalconstrain of ”orientation”. in the modified region developing, thereare thresholds; one is for the intensity and the other for orientation.region is grown if best each constrains are met. For evaluation ofthe propose technique, spilt the unique photograph into four, 18and 24 grids. Gridding results in smaller grids in order that anal-ysis can be carried out effortlessly that’s shown in figure 5.in thistechnique each of the grids is treated one at a time to which thevicinity growing method is implemented. The preliminary step inarea growing for the grid shaped is to select a seed factor for thegrid. The initial region starts with the exact place of the seed.

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also to discover the seed factor of the grid histogram evaluation isachieved. The histogram is observed out for every pixel in the grid.Because the picture is a gray scale photograph, the values of thisphotograph is from zero to 255. For every grid, the histogram feethat comes most common is selected because the seed point pixel.From this, someone of the seed point pixel is taken because theseed factor for the grid. After finding out the seed point, the regionis grown from it. The neighboring pixels are in comparison withthe seed factor and if the neighbor pixel satisfies constrains, thenthe area is grown else it isn’t always grown to that pixel. parent 6suggests 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 inputMRI image, in this approach use the neural network classifier tocategorise the picture into tumourous or not and its disorder typeadditionally. The neural network consists of three layers whichcan be enter layer output layer. first of all the neural networksare educated by the features which might be extracted inside theprevious step. For the schooling cause, we have used about 30

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Figure 7. Segmentation with Proposed Modified Region growingmethod

MRI snap shots of which 15 are normal and the alternative 15 aretumourous. The neural community is skilled with functions of thosesnap shots. we’ve applied Feed ahead Neural community (FFNN)and Radial basis characteristic (RBF) neural network for compar-ative evaluation. The enter MRI photo is fed into the trained neu-ral network after the pre-processing and changed location growing.The classifier compares the educated information with those of theenter picture feature records and classifies it into tumourous or reg-ular.

4 Conclusion

The method includes pre-processing, segmentation; function ex-traction of the place and final type. The normal area developinghas the drawback of noise or variant of intensity which may addi-tionally bring about over segmentation. to overcome this downsidea further constrains of ”orientation” is brought inside the changedlocation growing. with the aid of analyzing the effects, the per-formance of the proposed approach has drastically advanced thetumour detection as compared with the vicinity growing algorithmbased MRI segmentation.

References

[1] A.R.Kavitha,Ms.Kavin Rupa, Dr.C.Chellamuthu An EfficientApproach for Brain Tumour Detection Based on Modified Re-gion Growing and Network in MRI Images, 2012

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[2] H. B. Kekre, Kavita Raut, Tanuja Sarode, ”Detection of Tu-mor inMRI Using Vector Quantization Segmentation”, Inter-national Journal of Engineering Science and Technology, Vol.2, No: 8, pp: 3753-3757, 2010.

[3] Jue Wu, Albert C.S. Chung, ”A novel framework for segmenta-tion of deep brain structures based on the Markov dependencetree”, Neuro Image, Elsevier, vol: 46, pp: 1027-1036, 2009.

[4] S. Klein, et al. ”Early diagnoses of dementia based oninter subject whole- brain dissimilarities,” Proc.IEEE In-ternational Symposium on Biomedical Imaging: FromNano to Macro, IEEE Press April 2010,pp. 249-252,doi:10.1109/ISBI.2010.5490366.

[5] E. A. El-Dahshan, T. Hosny, and A. M. Salem, ”Hybrid intel-ligent techniques for MRI brain images classification,” Journalof Digital Signal Processing, vol. 20(2), March 2010, pp. 433-441,doi: 10.1016 /j.dsp.2009.07.002.

[6] S.Chaplot, L. Patnaik, and N. Jagannathan, ”Classificationof magnetic resonance brain images using wavelets as inputto support vector machine and neural network,” BiomedicalSignal Processing and Control, vol. 1(1), 2006, pp. 86-92.

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brain tumor segmentation using deep neural network · brain tumor segmentation using deep neural...

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