Volume 5, Issue 9, September 2019 (ISSN: 2394 – 6598)
BRAIN TUMOR DETECTION USING ANN CLASSIFIER
1Francy Irudaya Rani. E, 2Niranjana. R, 3 Abirami.M, 4Grace Priya.A, 5Indiranatchiyar.A
1,2Assistant Professor, 3,4,5 UG Scholar,
1,2,3,4,5 Departmet of ECE, Francis Xavier Engineering College, Tirunelveli, Tamilnadu, India
ABSTRACT
Segmentation of organizational segments of the brain is the essential problem in medical image investigation. This paper reviews several existing brain tumor segmentation and detection methodology for MRI of brain image. Altogether the phases for detecting brain tumor have been discussed comprising pre- processing steps. Pre-processing involves several operations like non local, diagnostic correction methods; Markov random field methods and wavelet based methods have been discussed
Keywords—ANN Classifier, Analytic Correction, Brain Tumor Segmentation, Cerebrospinal fluid, MRI.
1. INTRODUCTION
A brain tumor is a mass of abnormal cells budding in the brain. Primary brain tumors are those that initiate in the brain and be likely to stay in the brain. Metastatic brain tumors begin as a cancer elsewhere in the body and migrate, to the brain.
There are further than 120 different types of brain tumors; some are malignant (cancer), rest is nonthreatening or benign which are usually noncancerous. The basis of brain tumors is unknown.
Benign or malignant, primary or metastatic, brain tumors can be treated. Added knowledge about brain tumors has been gained in the last ten years than in the past hundred years due to involvement of high resolution techniques like functional MRI (fMRI), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) in medical imaging. Imaging practices to diagnose, stage, and follow patients with brain tumors are central to the clinical management. MRI is among the most universally used technique for lesion detection, definition of extent, revealing of spread and in evaluation of either enduring or recurring disease.
The procedure of segmenting tumors in MR images as
contrasting to natural scenes is particularly challenging. The
tumors fluctuate prominently in size and position, have a variety of shape and appearance properties, have intensities overlapping with normal brain tissue, and time and again a getting bigger tumor can deflect and deform nearby structures in the brain giving an abnormal geometry also for healthy soft tissue. For that reason, in general it is difficult to segment a tumor by simple unsupervised thresholding. Alternatively, different machine learning (ML) classification techniques have been explored:
SVMs (Support Vector Machines), MRFs (Markov Random Fields), and most recently CRFs (Conditional Random Fields).
Apart from the work on classification techniques, recently discrete segmentation approaches using graph cuts have been proposed for brain tumor segmentation. On the other hand among the continuous approaches, the variation and level set methods have also been explored with considerable interest in the past few years.
Therefore, it is indispensable to develop set of rules to obtain robust image segmentation such that the following may be observed:
• Automatic and semi-automatic demarcation of areas
to be exposed to radio-surgery.
• Outlining of tumors before and after surgical or radio- surgical intervention.
• Tissue grouping: Dimensions of white matter (WM), Grey matter (GM), Cerebrospinal fluid (CSF), Skull, Scalp and abnormal tissues.
2. GENERAL METHOD OF BRAIN TUMOR DETECTION
A medical image may be distinct as a two-dimensional smooth function, f (x, y) where x and y are spatial (plane) coordinates, and the breadth of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point.
When (x, y) and the breadth values of f are all finite, discrete quantities, we call the image a digital image. The arena of digital image processing discusses to processing digital images by means of a digital computer. Note that a digitized medical image is collection of a finite number of elements, each of which has a particular position and value. These elements are stated to as picture elements or pixels.
2.1 Image Segmentation Approach
There are more than a few types of segmentation possible to segment a tumor from MRI of brain, those segmentation have several rewards and drawbacks. These rewards and drawbacks have described very carefully with output are describe here. There are no such set of rules which always yield very good results for all type of MRI of brain images, thus a brief synopsis for different type of splitting up are discussed here. Though optimal selection of features, tissues, brain and non–brain origins are considered as main technical hitches for brain image segmentation.
There are several typical MR Image segmentation approaches as follows:
2.2 Thresholding Technique
Thresholding Technique is the classification of each pixel depends on its own information such as intensity and color information. Those procedures are proficient when the histograms of objects and background are clearly separated. In
thresholding based segmentation scheme, each image is divided into a number of segments by defining some threshold value. Thresholding is a very known and simple approach for segmentation in computer vision and image analysis. As the computational complication is low, thresholding centered system is considered for real time computer vision systems.
Thresholding scheme is broadly classified into two categories:
• Contextual (depends on second order gray level statistics or co-occurrence matrix of the image).
• Non-contextual (depends on gray level distribution of image).
2.3 Region Growing Technique
The concept of Region-based segmentation is to extract features (similar texture, intensity levels, homogeneity or sharpness) from a pixel and its neighbors is utilized to derive relevant information for each pixel. Region growing approach of segmentation is a well-known spatial segmentation scheme in image analysis and computer vision. In this scheme segmentation starts with some initial seed point selection using some predefined criteria. These seed points act as the initial points of different regions available in the considered image.
As the region growing process starts, the neighbouring pixels of a seed point from a particular region are tested for homogeneity and are added to a particular region. In this approach each pixel in that image is consigned to a particular region in the segmented image. After region growing is concluded region merging is executed, different regions of the image are merged to a single region with some similarity criteria. Region growing technique is simple and can correctly separate the image pixels that have similar properties to form large regions or objects. As this approach depends on the spatial correlation of pixels in an image, the segmented output is expected to be better as compared to the histogram thresholding based scheme.
2.4 Watershed Technique
Watershed algorithm is another approach of performing region based spatial segmentation, using gray level morphological operation. The intuitive idea underlying
watershed algorithm comes from the field of water topography:
a drop of water falling on a surface follows a descending path and eventually reaches to a minimum. Watershed lines are the dividing lines of the domains of attraction of these drops of water. An alternative approach is that where it is imagined that the surface is being immersed in a lake, with holes pierced in local minima. Water will fill up basins starting at these local minima, and at points which are considered where waters coming from different basins would meet, dams are built.
Therefore the surface is partitioned into regions or basins separated by dams, called watershed lines.
An early work using watershed segmentation scheme for object detection is proposed by Johan Debayle, Jean- Charles Pinoli [10], where watershed algorithm is used for object boundary identification and to avoid over- segmentation. Watershed segmentation performed to extract good markers of its objects, the cells. The watershed transformation is a very powerful morphological operation and whose use for segmentation was described and illustrated in numerous recent publications.
Given a set of markers and an image f regarded as a topographic relief, “marker-driven” watershed segmentation allows the image expert to remove the highest crest-lines of f separating two markers. Usually, one chooses for f a gradient-image, since the object's contours is usually understood as the highest crest- lines of the gradient around the object's marker.
2.5 Edge Extraction Technique
Edge-based approaches are concentrated on detecting contour. They fail when the image is blurry or too complex to identify a given border. The most important feature in an image is the contrast. Contrast may be described as discontinuities in the gray values of an image or variations in scene illumination.
In vision based analysis edge is well-thought-out as a very effective descriptor of contrast.
2.6 ANN image segmentation techniques
Artificial Neural Network Image Segmentation Procedures initiated from clustering algorithms and pattern recognition methods. They commonly aim to develop unsupervised segmentation algorithms. Sometimes, the above segmentation approaches are overlain and can be collective.
Several brain MRI segmentation techniques using neural networks are reviewed in literature [15]-[17]. The most famous unsupervised approach using ANN, the self-organizing feature maps (SOFM), developed by Kohonen [18] is a strong candidate for continuous valued unsupervised pattern recognition.
3. PROPOSED SYSTEM
This work will detect the tumor and identify the level of the tumor. For that here we use ANN(Artificial Neural Network) classifier for classification with high accuracy level. Finally the output of the brain tumor depth is displayed in 3- Dimensional picture
Fig3.1 Proposed Block Diagram
3.1 Pre-processing and Segmentation
Inp Pre Seg Fea Mo
ture
Tu Y D
N AN
Segmenting the image Input Image
RGB to Gray
Noise Removal
Edge Detection
Smoothing
Fig 3.2 Flowchart of Preprocessing for segmentation Step 1: RGB to gray
The input image is RGB that can be converted into gray scale image: rgb2gray(orig). In Gray scale image intensity of image range from minimum of ‘0’ as weak gradient to a maximum of
‘255’ as strong gradient. Therefore there is a need to convert RGB to Gray scale image.
Step 2: Noise Removal
Noise is removed on the image using the median filter:
medfilt2().median filter is used to preserve the edges. In signal processing it is often desirable to be able to perform some kind of noise reduction on an image or signal. The median filter is a non-linear digital filtering technique, often used to remove noise. Median Filtering is very widely used in digital image processing. Because, it preserves edges while removing noise.
Step 3: Edge Detection
Edges are the boundary of an image this is identified using the sobel operator: edge(). Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in 1D signal is known as step detection and the problem of finding signal discontinuities over time is known as change detection.
Step 4: Smoothing
Smoothing is used to preserve the edges from discontinuity. It uses the masking process: msk[].Smoothing may be used in two important ways that can aid in data analysis.
By being able to extract more information from the data as long as the assumption of smoothing is reasonable and by being able to provide analyses that are both flexible and robust. Many different algorithms are used in smoothing. Data smoothing is typically done through the simplest of all density estimators, the histogram.
Step 5: Segmentation
Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted.
3.2 Morphological Process
Morphological techniques probe an image with a small shape or template called a structuring element. The structuring element is positioned at all possible locations in the image and it is compared with the corresponding neighbourhood of pixels.
The structuring element is a small binary image, i.e. a small matrix of pixels, each with a value of zero or one:
• The matrix dimensions specify the size of the structuring element.
• The pattern of ones and zeros specifies the shape of the structuring element.
• An origin of the structuring element is usually one of its pixels, although generally the origin can be outside the structuring element.
3.3 Feature extraction
The major goal of feature extraction is to extract a set of features, which maximizes the recognition rate with the least amount of elements and to generate similar feature set for variety of instance of the same symbol. The widely used feature extraction methods are Template matching, Deformable templates, Unitary Image transforms, Graph description, Projection Histograms, Contour profiles, Zoning, Geometric moment invariants, Zernike Moments, Spline curve approximation, Fourier descriptors, Gradient feature and Gabor features
3.4 ANN Classifier
Artificial neural network is an example of supervised learning. Artificial neural network acquired the knowledge in
the form of connected network unit. It is difficult for human to extract this knowledge. This factor has motivated in extracting the rule for classification in data mining. The procedure of classification is starts with dataset. The data set is divided into two parts: training sample and test sample. Training sample is used for learning of network while test sample is used for measuring the accuracy of classifier. The division of data set can be done by various method like hold-out method, cross validation, random sampling. In general learning steps of neural network is as follows: Network structure is defined with a fixed number of nodes in input, output and hidden layer and an algorithm is used for learning process. The ability of neural network to make adjustment in structure of network and its learning ability by altering the weight make it useful in the field of artificial intelligence.
ALGORITHM
Input: dataset D, learning rate, network. Output: a trained neural network.
Step1: Receive the input.
Step2: Weight the input. Each input sent to network must be weighted i.e. multiplied by some random value between -1 and +1
Step3: Sum all the weighted input.
Step4: Generate output: the output of network is produced by passing that sum through the activation function
4. RESULT AND DISCUSSION
Here we conclude our work with detection of depth of the tumor by using 3-dimensional image.
4.1 Input image
4.2 Pre-processing
4.3 Segmentation
4.4 Morphological process
4.5 Feature extraction
4.6 3-D graphical representation
4. CONCLUSION
The undesirable background removal are important for Quality enhancement and filtering such as edge sharpening, enhancement, noise removal to obtain improved image quality as well as the detection procedure. Among the different filtering techniques deliberated in above mentioned techniques, median filter suppressed the noise without blurring the edges and it is superior outlier even without dropping sharpness of the images, mean filter are much greater sensitive than that of median filter in the circumstance of smoothing out the image. Gaussian reduces the noise; enhance the image quality and computationally further efficient as compared to other filtering methodology. After the several image quality improvement and noise reduction discussion here, several of the possible segmentation methodology like binarized segmentation based on intensity, based on Region, based on classification, based on texture, based on clustered, based on neural network, based on fuzzy, based on edge, based on atlas, based on knowledge, based on fusion, based on probabilistic segmentation has been briefly
described above with short details, advantage and disadvantage to detect or segment a brain tumor from MRI of brain image.
Most of the binarized fails due to large intensity difference of foreground and background i.e. the black or dark background of MRI image. The region growing methodologies are mostly not included in standard methods for validate segmentation; the main problem includes low quality of segmentation in the border of tumor. These methods are good for homogeneous tumor but not suitable in case of heterogeneous tumor. Segmentation techniques that are based on classification segment tumor accurately and produce good results for large data set but display undesirable behaviors can in those case where a class is under represented in training data. Clustered based segmentation provides quite simple, quick and yield good results for non-noise image but for noise images it leads to severe erroneousness in the segmentation. In neural network based segmentation performance is little better on noise field and there is no need of assumption of any necessary data distribution but learning process is one of the great disadvantages of it. In case of edge based segmentation better performance is given by canny but other noise edges are also produce, sober gives very effective outcomes but shortcoming of it is the discontinuity of edges. In the case of a weak border or when any gap is present in the border of the tumor, the contour or surface area may outflow to extra regions. Even in presence of several detriments, the atomization of brain tumor segmentation by means of grouping of procedures based on threshold technique and grouping like SVM, Basian may overcome the problems and offers actual and precise results for brain tumor detection.
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