Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review

GRD Journals- Global Research and Development Journal for Engineering | Volume 2 | Issue 4 | March 2017 ISSN: 2455-5703

Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review Ankita Kapil Student Jabalpur Engineering College, Jabalpur, MP India

Dr. (Mrs.) Shailja Shukla Professor Jabalpur Engineering College, Jabalpur, MP India

Abstract Medical image processing has dramatically revolutionized the health care sector by helping clinicians towards early and accurate diagnosis of the disease. MRI is one of the most versatile and widely used imaging modality. Brain tumours are the abnormal growth of tissues and this can be benign and malign subject to the tumour location and size, which are often difficult to visually diagnose from MRI film. Therefore, several image processing techniques have been developed and in use for accurate and early detection. Image Segmentation typically applied to detect a specific region in an image and is often used in biomedical field. One of the most challenging implementation of this technique is in brain tumour recognition. This paper presents a comparative study of different segmentation techniques for extraction of tumour from MRI images.

Keywords- Image processing, brain tumour, Image segmentation, Magnetic resonance imaging (MRI)

I. INTRODUCTION Medical image processing is a challenging and a crucial field, the analysis of the taken medical image is very important task for further diagnosis of the disease in patients. There are several techniques that help in the deformed object recognition in these images [1][2][3].A brain tumour is a mass of abnormal tissues or cells in the brain. A tumour can be benign or malignant. Benign tumours are the grade 1 or 2 type of tumours, they are not cancerous and do not grow back after treatment and diagnose. The malignant tumours are grade 3 or 4 type of tumours, which contains cancerous cells, malignant tumours grow rapidly and spreads in brain and surrounds the brain tissues, there are over 120 types of tumours depending on their generation part, size, origin of cells from they are growing and other causes[3]. Different type of tumours affects different part of brain which may cause in person behavior, health, and senses. The diagnose can be carried out by knowledge of medical imaging [4], MRI, CT Scans, Electroencephalography (EEG). MRI has advantages over CT scans being capable of detecting the flowing blood and cryptic vascular malformation. MRI is able to visualize the brain in all three planes Axial, Sagittal, Coronal which gives a good view of brain as given below in figure.

Fig. 1: Three axis views of brain (axial, sagittal, coronal)

For brain tumour identification usually the T1- T2 weighted images are used and they are differentiated by looking at their csf (cerebrospinal fluid), in T1 – weighted images csf is dark and bright in T2 – weighted images. Contrast type T1 images are obtained by applying gadolinium, it makes the image bright. MRI images are used widely to detect the tumours in brain, for noise removal in different images filtering is applied and for different methods an analysis is shown here in [5][6], there are many methods performed on MRI images like segmentation, morphological operations, feature selection and extraction, classification etc.

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Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review (GRDJE/ Volume 2 / Issue 4 / 003)

II. LITERATURE REVIEW Segmentation of MR images divides the regions containing pixels with same attributes for the meaningful and useful analysis.

Multi-channel MR images with varying contrast characteristics can be acquired, providing additional information for Image Segmentation for distinguishing between different structures. For general reviews on the segmentation of MR images, see [7-10].

The techniques of segmentation can be generally classified as Thresholding, Region growing, Classifier, Clustering, Markov random analysis, ANN, Atlas guided approach. In this paper some techniques are discussed a reviewed.

A. Thresholding Thresholding in image segmentation converts a grayscale image into a binary image, it divides the different attributes in an image between foreground and back ground, say an image is converted into a black(0 value pixel), and white(1 value pixel).If g(x, y)is a thresholded version of f(x, y)at some global threshold T, 1 if f(x, y) > 0 g(x) = { 0 otherwise Morphological operations (Erosion, Dilation) are applied on the image obtained after Thresholding. Below the image shows global Thresholding on brain tumour MRI.

Fig. 2: a) brain MRI b) after global Thresholding c) final tumour detection after morphology In [11]Natarajan Prem et al.represented detection of brain tumour by MRI images using pre-processing where image sharpening is performed by median filters image enhancement by histogram , segmentation by Thresholding and morphological operations , and finally detection of tumour by image subtraction. As result the tumour is detected by Thresholding and the operations performed and the time taken not considered here. In EEG also Thresholding removes artifacts and gives better result Roy, Vandana, and Shailja Shukla [12].

B. Region Growing Based The goal of region growing is to use image characteristics to map individual pixels in an input image to sets of pixels called regions.

The region corresponds to a world object or a meaningful part of one. When the pixels are arranged on the basis of their properties and neighbouring pixel properties its local technique, when pixels are grouped and formed a big region from whole image it is a global technique, and in splitting and merging an individual pixels or set of pixels below the figure shows the example of region growing based segmentation.

Fig. 3: diffusion- weighted brain image (left) result of region growing segmentation (right)

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Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review (GRDJE/ Volume 2 / Issue 4 / 003)

In Zabir, Ishmam, et al. [13] the region growing is automatically selected as initial contour to the iterative distance regularized level set evolution method so the need of selection of the region of interest by the user. A completely computer aided automated technique is developed to detect glioma the BRATS 2012 database is used which contains different glioma cases. T2 and flair images are used and final contour after 50 iterations both the images are summed up to find final segmented tumour, the rese performance was 55-95% dice coefficient for different cases.

C. Clustering Based A cluster consist a group of similar objects, clustering is of two types supervised and unsupervised, in supervised clustering criteria is decided by user but in unsupervised clustering criteria is decided by the system itself.

D. K- Means Clustering K-Means is an unsupervised clustering algorithm that classifies the input data points into multiple classes based on their inherent distance from each other. Let X ={x1, x2, x3‌xn} represents a set of elements of an image and C= {c1, c2, c3‌ck) represents asset of centroid, where is number of clusters. The main objective of k means clustering is to minimize an objective function J that determines the closeness between the pixel and the cluster centroids, and is calculated as follows, J= ∑đ?‘›đ?‘–=0 ∑đ?‘˜đ?‘—=0 ‖đ?‘Ľđ?‘–đ?‘— − đ?‘?đ?‘— ‖2 CC Lin, CC Chang. [14], in their work k - means converts the grayscale image into a colour space image and then the position of tumour is separated from the other objects in the MR image, first the grayscale image converted into RGB colour and then further converted into CIELab colour model, they concluded that the method is easy and efficient at that time.

E. Fuzzy C- Means Clustering fuzzy c means clustering allows the objects to belong to one or more clusters, in FCM algorithm number of elements partitioned into c fuzzy clusters with given range or criteria. Chuang, Keh-Shih, et al. [15], applied the FCM algorithm that includes the spatial information into the membership function to fully discover the characteristics of the image, the advantage of doing this yields the region homogenous than the other methods, removes the noisy spots, the result showed that the noise was less with the new algorithm in comparison with the conventional and Benson, C. C., et al. [16] represented a method to segment brain tumour where the FCM and watershed together is applied. FCM clustering is applied for initial centroid selection based on histogram calculation and in watershed an atlas based marker detection method was applied to reduce over segmentation, the result found here was accurate from 93.13 and 88.64 % of dice and tanimoto coefficient values.

F. Watershed watershed is another region based method that has its origin in mathematical morphology, general concept was introduced by H. Digabel and C. LantuĂŠjoul in 1977,[17] watershed segmentation is based on the idea of regarding an image as topographic landscape with ridges and valleys. K. Bhima and A. Jagan [18], this paper concentrate on improving the detection of brain tumour in MRI by using marker based watershed segmentation in few dataset MRI images, followed by MATLAB implementation as testing results they found better results, the brain tumour was clearly segmented. I. Maiti and M. Chakraborty,[19], brain tumour detection based on colour by using watershed along with edge detection algorithm, the result found was promising, it was based on HSV colour madal.P. Dhage, M. R. Phegade and S. K. Shah [20], the paper proposed a method based on watershed segmentation that separates the abnormal tissues of brain from the normal one, while the tumour is extracted but the position and shape is also determined and other parameters like eccentricity, entropy and centroid is also calculated.

G. Atlas Based Segmentation In atlas based segmentation data obtained from different subjects arranged as common anatomy an atlas (brain images) is constructed.SRI24 is an atlas of 24 normal brain subjects which is given by T. Rohlfing, N. M. Zahr, [21], an atlas is used as prior information in segmentation, in atlas based segmentation the image should be registered, I. Diaz and P. Boulanger[22], the proposed method uses nesh- free total lagrangian explicit dynamic algorithm for the simulation of atlas deformation and a data driven model of tumour using multi model MRI segmentation. The experiment performed on 12 MRIs taken from BRATS challenge training dataset, the method introduces a novel approach of atlas to patient registration method with tumour; the method used GUP to reduce computational time.

H. Artificial Neural Network The ANN is based on the process of biological nervous system. it resembles the functioning of human mind, this is an information dispensation model and are motivated by the way human intelligence system works. The basic processing element of ANN are artificial neurons, it is adaptive as changes its configuration based on internal or external information that flows from end to end through the system.D. M. Joshi, N. K. Rana and V. M. Misra[23], a brain cancer detection and classification system has been designed and developed that uses computer based procedures to detect tumour and lesions and classification of tumour is done by ANN.V. Kumar, J. Sachdeva et. al, [24] presented a method based on PCA-ANN along with GVF(Gradient vector flow) where the detection and classification of tumours done with high accuracy rates for different cases, the overall accuracy was 91.7%.

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Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review (GRDJE/ Volume 2 / Issue 4 / 003)

I. Markov Random Field An MRF modal spatially interacts with neighbouring or nearby pixels. MRF itself is not a segmentation method but a statistical model and can be used within segmentation method. In MRF method, the value of pixel is updated by iterated conditional modes and simulated annealing with maximizing a posterior estimate. Usually, it is computationally expensive method. Pan Lin[25] represented an efficient method, tissue classification method, which uses Bayesian contextual classifier based on Markov random field models. The Bayesian theory is used to generate probability maps for each tissue, which estimates the most likely tissue volume fraction within each voxel. All tests have been done in a ground truth image considering different noise levels. S. Abdulbaqiet. al. [26] proposed a method on HMRF and threshold technique that segments the brain MRI and tumour is detected by threshold technique with high accuracy.

III. COMPARARTIVE RESULTS OF DIFFERENT TUMOR DETECTION METHODS Table 1: Literature work Sr. NO.

AUTHOR

YEAR

METHOD

PRECISENESS

1.

Natarajan Prem et al.

[11]

2012

Thresholding

Successfully differentiated between affected and nonaffected brain MRI. (TUMOUR DETECTED).

2.

Zabir, Ishmam, et al [13]

2015

Region Growing

Detected tumour with accuracy between 55-95% dice coefficient.

3.

CC Lin, CC Chang.

[14]

2007

k- means clustering with histogram statistics

Method was very efficient, tumour location detected successfully

4.

Benson, C. C., et al.

[16]

2016

Fuzzy c means and watershed

Comparatively improved results found in detection of tumour from previous one.

5.

.K. Bhima and A. Jagan [18]

2016

Marker based Watershed

Detected tumour with high accuracy.

6.

I. Diaz and P. Boulanger[22]

2015

Atlas based

Tumour detected

7.

D. M. Joshi [23]

2010

Artificial neural network

Tumour successfully

8.

H. S. Abdulbaqi et. al.

[26]

2014

Hidden Markov random field with threshold techniques

Tumour segmented and detected with high accuracy.

IV. CONCLUSION AND FUTURE SCOPE There are different techniques for detecting tumours like neural networks, fuzzy means, marker based, K-means and histogram, region growing. Segmentation is faster and more accurate for tumour detection in MRI images. The segmentation is a process which converts any gray scale image to binary image because analysis and computation on binary images are simpler. Segmentation of medical images reduces the chances of fault and detects the tumour and lesions with good accuracy rates, different segmentation techniques have been discussed above with some good detection rate while in each case some improvement is also required as in future we are going to introduce a method which is more efficient to detect tumour with high accuracy and less computational time.

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