DETECTION OF TUMOUR IN MRI IMAGES OF BRAIN USING SPEARMAN ALGORITHM

Volume 5, Issue 7, July 2019 (ISSN: 2394 – 6598)

©IJETIE 2019

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DETECTION OF TUMOUR IN MRI IMAGES OF BRAIN USING SPEARMAN ALGORITHM

Kiruthika Lakshmi V, C.Amarsingh Feroz PG Students, Assoc.Professor/ECE,

Francis Xavier Engineering College, Tirunelveli-627003, Tamil Nadu, INDIA.

[email protected], [email protected]

ABSTRACT

This project investigates the problem of image classification with limited or no annotations, but abundant unlabeled data. The setting exists in many tasks such as semi-supervised image classification, image clustering, and image retrieval. Unlike previous methods, which develop or learn sophisticated regularizes for classifiers, our method learns a new image representation by exploiting the distribution patterns of all available data. Particularly, a rich set of visual prototypes are sampled from all available data, and are taken as surrogate classes to train discriminative classifiers; images are projected via the classifiers; the projected values, similarities to the prototypes, are stacked to build the new feature vector. The training set is noisy. Hence, in the spirit of ensemble learning we create a set of such training sets which are all diverse, leading to diverse classifiers i.e. Deep learning neural network and RBFNN classifier. It is conceptually simple and computationally efficient, yet effective and flexible. This project is implemented using Matlab simulation.

Keywords—Brain tumour, Radial basis function Neural Network, Spearman algorithm.

1.

A Brain Tumor is a mass or growth of abnormal cells in the brain. Some of them are Non-Cancerous, slow growing and does not spread to the surrounding areas called Benign.

Some of them are fast growing, aggressive and spread to the nearby tissues called Malignant. The tumours ate classified in to primary and secondary tumors. The primary tumor originate in the brain itself or closes to its membrane called Meninges. The example for this tumor are Gliomas, Meningiomas etc. In the secondary tumor cancer can occur in other parts of the body and spread to the brain. The examples of this tumor are breast cancer, kidney cancer.

Headache, Nausea, Muscle jerking, Numbness, Changes in mood, personality are the symptoms of brain tumor.

Traditional surgery, Chemo therapy, Radiation therapy and Stereotactic radio surgery are the common treatments of brain tumour. In this paper, Gaussian filter is used for pre- processing the input images, Spearman’s algorithm is used for segmentation process and Radial basis function neural network is used for classification purpose. This paper is structured as pursues. Part 2 gives a short summary of the associated works. Part 3 elaborates about the Planned work.

Part 4 examine the outcome. Finally, this paper is finished

up with the conclusion and thought regarding the future work.

2.

In manual segmentation, tumor areas are manually located on all contiguous slices in which the tumor is consider to exists. It is expensive, time consuming and tedious tasks.

Convolutional neural networks are used for the evaluation of performance for automatic medical image segmentation.

But ordinary CNN doesnot produce accurate and robust results for clinical use. To overcome this problem, we use Deep learning based iterative segmentation framework. This can be achieved by incorporating CNN in to a boundbox and scribble-based segmentation. The CNN with bounding box can be either unsupervised or supervised and weight loss function is used for image tuning. In the testing stage, the user uses the bounding box. The BIFseg extracts the region inside the bounding box. The extracted region is then fed in to the pre-trained CNN for initial segmentation. This is how the CNN are designed and trained to observe some features such as saliency, contrasts and hyper intensity. Validation can be performed in two applications such as 2D segmentation of multiple organs and 3D segmentation of brain tumour. Experimental results show that our model is more robust and provide better accuracy and it uses less time

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©IJETIE 2019 than traditional interactive segmentation methods. The main

problem of this method is decrease in speed and accuracy.

3.

The proposed work is based on automated algorithm used for tumour detection and segmentation. The automated algorithm used in proposed work is based on Spearman algorithm which is the unbiased optimization technique used for image enhancement and segmentation. The basic features are extracted by using GLCM. The data are compared by using RBFNN classifier.

3.1 Block Diagram

Figure 3.1 Block Diagram of Brain tumor detection Frame Work.

3.2 Description of Block Diagram

The MRI images of patient are considered as test image. If the test image is color image then convert it in to grey scale image. The input image is given to the pre-processing block in order to extract the needed portion from an image. The pre-processed image is then segmented in to 256 samples.

In order to find the amplitude values, discrete wavelet transform is used. Features are extracted by using fuzzy clustering. Classification can be carried out by using RBFNN classifier. Based on the obtained features the RBFNN Classifier is used to compare trained and test data sets. Finally, the output image is generated showing whether the brain tumor is present or not. The block diagram of Brain Tomor Detection Framework is shown in Figure 3.1.

3.3 Pre-processing

The Pre-processing block is essential to remove the unwanted portions in an image and generate the essential portion for further processing. Gaussian filter is used in this project to remove the unwanted noises from an image. The main aim of pre-processing is needed to enhance the essential features of an image.

3.4 Edge Extraction

Edge extraction is used to extract the necessary information from the edges from an image. It is also used to reduce the unwanted information from an image. It is also used to conserve the structure of an image. It is used to extract the features such as lines, curves etc.

3.5 Gaussian filter

Pre-processing of input image is carried by using Gaussian Filter. Gaussian filters are used to remove noises by using Gaussian function. It is also used to enhance the contrast in an image. It represents the signal as a function of both frequency and time.

3.6 Segmentation

Segmentation can be carried by using Spearman algorithms. The wavelet transform is like a wave oscillation with amplitude that initializes out at zero, increases and then decreases back to zero. The wavelet transform has the advantage that it bound with both frequency and time whereas Fourier transform only bound to frequency.

Wavelets are used to extract information from the input MRI image.

3.7 Feature Extraction

The main purpose of feature extraction is used for enhancing an image. The proposed work uses Grey level co-occurrence matrix. Feature extraction is helpful in identifying brain tumor where is exactly located and helps in predicting next stage. Transforming the input data into set of features called feature extraction.

3.8 Classification

Classification is used to discriminate the objects based on their attributes. It is done by using supervised or unsupervised learning. Classification technique is used to classify the input image into normal and malignant.

Classification can be done by using various techniques such as KNN, locust based genetic algorithm, SVM, etc. In this project RBFNN classifier is used.

3.7 Radial Basis Function Neural Network

In the proposed work, RBFNN method is used for classification. It is one of the latest and most accurate methods used for classification. This method consists of input, Filter, Pooling and Convolution layer. The input layer consists of both test and trained images. The filter layer consists of filtered test and trained images. The pooling layer is used to separate the test and trained images. Then the test and trained images are compared and the output is displayed. During training stage, the genetic algorithm

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©IJETIE 2019 selects the suitable margin between two classes. Locust

based genetic algorithm trains itself by features given as an input to its learning algorithm. The trained data set consist of 256 samples of the reference image.

4.

The Figure 4.1 shows the input image also called test image.

The test image may be color or black and white image. If the input image is black and white, then it is directly given to next block.

Fig.4.1 Input Images

Fig.4.2 Pre-processed output of input Image

Figure 4.3 Segmented output of input

Figure 4.4 Edge detected output of input Image

Figure 4.5 Classified Images

If the input image is colour image convert it in to a grey image by using appropriate function. The pre-processed image is appeared in Figure 4.2. Figure 4.2 shows the pre- processed image by using Gaussian Filter. Partial identification is achieved by using this filter. Figure 4.3 shows the segmented image by using Discrete Wavelet Transform. Exact identification of malignant or benign is achieved by using spearman’s algorithm. Figure 4.4 shows edge detected image. In this figure edges are detected in order to provide the accurate results. It is used to preserve the structure of an image. In this figure classified output is shown. Classifier is used to compare the test and trained data and show the accurate result whether the tumor is present or not. Classified output image is obtained and shown in Figure 4.5.

4.2 Comparison of RBFNN Classifier with CNN Classifier

The main advantage of RBFNN is no need to have large trained data set. Low memory space is enough to maintain the data set. The RBFNN provides more accurate results. In this figure the accuracy value of RBFNN is

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©IJETIE 2019 compared to the CNN classifier accuracy. The above figure

shows the execution time analysis of RBFNN classifier to the CNN classifier.

Figure 4.6 RBFNN VS CNN (ACCURACY)

Fig.4.7 EXECUTION ANALYSIS

5.

The brain tumour cells are grown in the brain. A band of ligament from human body formed by uncontrollable development and splitting of tumorous cells is known as tumor or lump. Human body’s total metabolism function is affected by tumor. Magnetic Resonance Images (MRI) is the known technique in analysis of tumor. Prediction of affected area using MRI images is time-consuming as well as error- prone process and this analysis supports to enhance existing automated interaction model. Machine learning algorithms help the clinical professionals in identification of tumour affected region. One of the trademark techniques of CBIR listed as support vector machine (SVM), neural network, Maximization algorithms are famous for interesting points based region identification and classification. The improvement of new demonstration tools for image processing that can examine various features and be implemented by accurate learning algorithms is the mostexpected research work in image processing community. The multiresolution classifier design in this thesis is very hopeful and leads to an assortment of possible extensions. The future work regarding image recognition

can be determined towards optimization in searching of the image retrieval process. Future work can also be focused on the scope of the database under concern, where storage of retrieved results for future comparisons is to be customized and manageable with growing database.

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