Brain Mri Image Segmentation using Improved Sobel Method

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International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7 Issue-4, November 2018

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Abstract: Health care applications need correct segmentation on medical images which helps for correct diagnosis. Good quality segmentation can be done only by an efficient method. In this paper we have studied and evaluated three different segmentation techniques. We have presented an improved edge segmentation method for brain MRI images. The improved sobel algorithm makes use of sobel method with closed contour algorithm which will combinely help in maintaining uniformity in the regions. Image dependent method of thresholding helps in closed contour to fix up clear boundaries of different regions in an image. The algorithm is implemented in Matlab and performance is measured subjectively as well as objectively. For comparative analysis, we have used entrophy, correlation and energy of the three segmentations which show improved sobel method is better compared to watershed segmentation and sobel segmentation.

Index Terms: Sobel segmentation, Watershed Segmentation, Improved Sobel Segmentation.

I. INTRODUCTION

Image segmentation plays major role in Image analysis and is widely used in many applications like medical, sports, security and remote sensing satellites etc. Segmentation divides an image into many subparts or objects. In this process segmentation play role in separating tumor from MRI brain image. As number of patients is large, manual detection of tumor needs experienced radiologists and segmentation of large data is time consuming and is also susceptible to errors [1]. Radiologists study the anatomical structures, tumors, tissues in brain MRI to detect the type and reason for the tumor, so that treatment can be planned accordingly. But the collected MRI of the patient may have non linear characteristics and may be corrupted with noise. Thereby radiologists find difficult in studying the abnormal growth of glands and finding the location and size of tumor or tissue [2].

Distance measured based on intuitionistic fuzzy sets decrease the image dependency on noise and intensity non homogenity (INU) and hence helps accurate tisuue segmentation of brain [10]. Generalized rough intuitionistic fuzzy means (GRIFCM) segmentation uses histogram for initial centroid calculations and becomes independent from dependency member functions, parameters. Thus GRIFCM handles INU and noises. Hence the drawbacks of dependency on parameter tuning and fuzzy generators are removed.

Finding optimum value in multilevel threshold is time

Revised Version Manuscript Received on 30 November, 2018.

P.Nagabushanam, Department of EEE, Karunya Institute of Technology and Sciences, CBE, India.

S. Satheesh kumar, Department of EEE, Karunya Institute of Technology and Sciences, CBE, India.

J.Sunitha Kumari, Department of ECE,TKR TKR College of Engineering

& Technology, Hyderabad, India.

S.Thomas George, Department of EEE, Karunya Institute of Technology and Sciences, CBE, India.

consuming for which two swarm optimization algorithms namely Moth Flame Optimization (MFO) and Whale Optimization algorithms (WOA) are proposed [11]. Otus method is used as the fitness function and aim of the algorithm is to give maximum value for the fitness function. At threshold value three, both the proposed algorithms give best results compared to other swarm algorithms in terms of PSNR and SSIM [11]. In [12], image is segmented using 3D Otsu method and then filtered using Laplacian filter in each iteration and then that output becomes input for next iteration.

Noisy images are also segmented using multilevel 3D thresholding. Then it is compared with other multilevel thresholding like particle swarm optimization, adaptive bacterial foraging etc. Precise segmentation is obtained in proposed 3D thresholding compared to 1D segmentation.

Fuzzy entropy and Shannon entropy is maximized using multilevel segmentation which depends on firefly algorithm.

Then it is optimized using bat algorithm, differential evolution and particle swarm optimization [13]. It gives better performance in terms of objective function, CPU time, and PSNR and similarity index. SAR segmentation with a fast algorithm and multilevel ratio of weighted averages used for selecting an optimum threshold value[15]. It gives better results by reducing number of arithmetic operations for selecting the optimum threshold value. In this work, we propose a new approach for segmentation in MR brain images based on the closed contour algorithm. The proposed method has two major contributions: The first one is to handle the grayscale image input according to a range analysis in the image intensity Scale with sobel operator and finding gradient image. The second contribution is the establishment of closed contour. These contributions to the sobel method lead to efficient segmentation in MR brain images. The results of the proposed method are compared against those of the sobel algorithm and those from the Watershed, which are commonly used in this field.

The rest of this paper is organised as follows: the related work of the proposed algorithm for image segmentation is discussed in Section 2. The various types of segmentations is presented in Section 3. Experimental results of the proposed algorithm are presented in Section 4. Conclusions of the proposed algorithms are presented in Section 5.

II. RELATED WORK

In [1], an improved edge detection called sobel edge detection is implemented. Then sobel method is combined with image dependent threshold method.

Brain Mri Image Segmentation using Improved Sobel Method

P. Nagabushanam, S. Satheesh Kumar, J. Sunitha Kumar, S. Thomas George

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Closed contour algorithm is applied to find different regions by which tumors are extracted within closed contour using image intensity information. The edge detected by improved sobel edge detection has less false edges. Closed contour algorithm helps in increasing region area and decreasing the thickness of boundary lines in regions.

Improved sobel edge detection gives better results compared to conventional edge detection methods in terms of gray level uniformity measure, Q parameter and relative ultimate measurement accuracy.

In [2], an adaptive fuzzy means clustering is used to segment the MRI brain images into three regions namely white matter, grey matter and cerebrospinal fluid spaces respectively. These regions are then analyzed to diagnose the brain disease. The proposed adaptive fuzzy means clustering is then compared with fuzzy means clustering in terms of mean square error and Borsotti functions F‟(I) and Q(I). In [3], four segmentation methods are reviewed namely region based, model based, machine learning and thresholding segmentations. Model based segmentation is mostly used method and thresholding gives better mean values. The largest white matter in brain is called corpus callosum, which plays major role in diseases related to central nervous system.

In [4], an adaptive k means clustering algorithm is used to segment a given brain MR image into k different tissues namely white matter, grey matter, CSF and many other abnormal tissues. Random selection of seed points does not give accurate results for segmentation. K means clustering is a partitioning algorithm, but has some limitations. In this paper vessel segmentation method along with data scale clustering leads to better centroid selection and prove to be a better clustering method. In [5], sub image segmentation and white blood cells location segmentation problems are addresses using a new approach. In this GrabCut algorithm, a rectangular region is extracted by scoring multiscale cues in which multiple windows are obtained. This proposed algorithm is applied on jiashan dataset and Cellavision dataset. Better precision rates and higher recall are obtained in this approach.

In [6], based on likelihood of normal distribution, improvements are made in level set method. For handling the grayscale image 40 HU is used for center level and 80 HU is used for window width on image intensity scale. Brain CT image is segmented using the above method to detect the damaged portion of brain by stroke. In this proposed algorithm, optimum level is set and initialized by analysing the density of brain CT image. In this paper the F-measure, accuracy and stability is more in the proposed algorithm.

Image enhancement is done effectively by histogram equalization. 1-D enhancement shell is obtained from 3-D enhancement shell by dimensionality reduction method [7].

Histogram enhancement gives better enhancement for high dynamic range images, underexposed images with low computational time. Point are scattered around centroid in histogram explosion whereas shell histogram equalization points are projected to new points from the origin. By projecting points from inner shell to outer shell, contrast of RGB image is increased. Gamut merging occurs when points are projected from outer shell to inner shell. In [8], novel background detection is coupled with sparse representation

model by which reconstruction errors are for saliency detection. This approach is more accurate compared to other methods.

III. SEGMENTATION

Splitting an image into pixels with similar characteristics is called segmentation. For image analysis, the features extracted should strongly relate to the object. Regions in an image after segmentation should have homogeneous and uniform characteristics like color, texture and grey level.

A. Sobel Segmentation

This is an edge detection method of segmentation which calculates gradient magnitude which depends on first derivative. It gives edge as the point where the gradient of the image is maximum. Sobel operator works as a local average operation and this smoothens the effect of noise. Sobel operator works on the location of pixel and gives better result compared to other edge detection methods of segmentation.

Steep intensity gradient or discontinuity in intensity function of an image leads to the existence of an edge.

Gradient of an image is defined as f(x,y) at (x,y) and is written as a vector below.

▼f(x,y) = [Gx,Gy]T

Where Gx = f(x+1,y) – f(x,y) and Gy = f(x,y+1) – f(x,y)

Sobel operator detects horizontal and vertical edges by a first order derivative using a pair of convolutional 3x3 operator.

-1 -2 -1 -1 0 1

0 0 0 -2 0 2

1 2 1 -1 0 1

Fig1. Sobel Mask

With respect to center pixel, sobel operator can be applied to its 3x3 neighbourhood. This is to find the gradient magnitude in its x direction and y direction using the approximations below.

Gx = -f(x-1,y-1) + f(x+1,y-1) – 2f(x-1,y) + 2f(x+1,y) – f(x-1,y+1) + f(x+1,y+1)

Gy = -f(x-1,y-1) + f(x-1,y+1) – 2f(x,y-1) + 2f(x,y+1) – f(x+1,y-1) + f(x+1,y+1)

B. Watershed Segmentation

Watershed transform is calculated on a gradient image but not on a gray scale image. Extracting a connected region from an image based on some predefined criteria is called region based segmentation. Watershed algorithm depends on mathematical morphology which takes image as a topographic surface and the altitude is given by intensity of grey scale, pixel determines the position. Image now have valleys and peaks with varying heights. A pixel is selected to act as a seed, and then an uniform connected region is identified with respect to seed pixel. Thus adjacent pixels with same features as that of seed will come as one region.

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This process continues until no pixel is left without being included in any of the regions and the entire logic is called segmentation. The region growing method is used in many applications namely brain MR tumor segmentation, stroke segmentation and brain lesion with diffusion weight MR images.

Morphological algorithms follow geological analogy and plays major role in watershed segmentation. The image which is to be segmented is considered as topographical surface, where the altitude values are from grey levels or intensity values. A minimum value in the altitude is represented by mj, it acts as a dip and is surrounded by higher land. The area around the minimum is represented by a catchment basin CBi (mij), water falling on it will flow to the minimum. At each pixel where two or more catchment basins meet, a dam is built and each minimum is surrounded by imaginary dams which delimits the catchment basins. These dams are called watersheds for the topographical surface [23]. This is an example of morphological operation which acts as an edge detector and it identifies edges or boundaries within an image.

Image data is the topographical surface and grey levels in gradient image is the altitude. Low gradient region is the catchment basins and region edges are the high watersheds.

Pixels in same region are connected by a single basin‟s region with minimum altitude and have homogeneous characteristics of the same catchment basin of topographic surface.

Segmented image regions are represented by such catchment basins.

The algorithm is briefly explained by three phases as below.

In first step pixels of gradient image G(I) are scanned for gradient minimum. For a pixel in G(I), define a set N representing the neighbours (x‟,y‟) for that pixel. When an eight connectivity is used x‟=x-1, x, x+1 and y‟=y-1, y, y+1. If G(x‟,y‟)x‟ < G(x,y) then y‟€ N(x,y). Now G(x,y) is named as Non-Regional Minima and put into First in First out Queue.

When Q is not empty, if the first element in queue is G(x‟,y‟) it will be popped out. If the label G(x‟‟,y‟‟) is void then x‟‟,y‟‟ € N(x‟,y‟) and G(x‟,y‟)=G(x,y). Now G(x‟‟,y‟‟) will be put in the queue Q.

In the second step all the adjacent pixels of minima are put in ordered queue and then all pixels of G(x,y) are scanned again starting from label i=1. If G(x,y) is void, and G(x,y) € CBi then G(x,y) is put in the FIFO queue. If queue is not empty its first element is popped out.

Let the first output element from queue be G(x‟,y‟) and if G(x‟‟,y‟‟) be void, x‟‟,y‟‟ € N(x‟,y‟ ) then G(x‟‟,y‟‟) € CBi.

G(x‟‟,y‟‟) is put in queue, otherwise G(x‟‟,y‟‟) is labelled as Non Regional Minima and put in ordered queue.

In the last stage the pixels in ordered queue with lowest gradient value is popped out. If the first output element in ordered queue is G(x,y) and G(x‟,y‟) is void, x‟,y‟ € N(x,y), then G(x,y) € CBk if G(x‟,y‟) € CBk for k = 1,2,… i.

The Watershed method plays major role in recent medical imaging applications, namely for abdominal MR images in liver segmentation [17], detection of cervical cancer [16] .The Watershed method also applied for stroke segmentation [20], brain segmentation [18], abnormal masses [19], brain tumor segmentation in MR images [21].

C. Improved Sobel segmentation

It depends on automatic threshold calculations and a four step methodology.

1. It uses sobel operator and finds the gradient image.

2. It calculates image dependent threshold on an iteration basis.

3. Then applies the closed contour algorithm.

4. Taking pixel intensity within closed contour into consideration, it finds the object segmentation.

For a given image sobel operator applies 3x3 mask and gradient value along x and y axis are calculated as follows

Hence the definition for gradient of an image is given by

Here i and j are the unit vectors along x axis and y axis respectively.

And the magnitude of gradient is calculated as G(x,y) = │▼f(x,y) │=

The next step is to find a threshold value for which all the edges can be determined. Initial threshold will be the average intensity of gradient image, and then the image is separated into two classes. The lower class consists of pixels with gradient value less than the threshold and upper

Table 1. Entrophy, Correlation and Energy of Sobel Method, Watershed Segmentation and Improved Sobel

Method Improved Sobel Method Energy 0.7001

Table 1. Entrophy, Correlation and Energy of Sobel Method, Watershed Segmentation and Improved Sobel Method Improved Sobel Method Energy 0.7001 0.5876 0.5902 0.7490

Corre lation 0.623 9 0.570 3 0.491 6 0.596 2

Entrop hy 0.5497 0.6852 0.6658 0.4723

Watershed Segmentation Method Ener gy 0.58 80 0.89 53 0.52 83 0.63 83

Correla tion 0.8488 0.6325 0.5702 0.7529

Entr oph y 0.78 58 0.24 17 0.76 28 0.67 61

Sobel Method Ener gy 0.69 44 0.65 87 0.71 84 0.73 44

Corr elati on 0.80 89 0.76 00 0.78 63 0.81 84

Ent rop hy 0.6 14 2 0.6 48 8 0.5 69 7 0.5 55 4

Origi nal Image Tumo r 1 Tumo r 2 Tumo r 3 Tumo r 4

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Class has pixels whose gradient value is greater than the threshold value. Now calculate the average gradient value of lower class (mL) and upper class (mU). The new threshold value is the average of mL and mU. Repeat this process until the threshold value is less than or equal to zero. The pixel value with gradient less than threshold

is called background point else it is called edge point.

IV. RESULTS AND DISCUSSION

Results show that in Fig 2d) & Fig 4d) regions of sobel segmentation does not give closed contour and hence false

Fig 1. Results of Sobel Method, Watershed Segmentation and Improved Sobel Method Extracted Tumor using Improved Sobel Method Fig 2f) Extracted Tumor using Improved Sobel Fig 3f) Extracted Tumor using Improved Sobel Fig 4f) Extracted Tumor using Improved Sobel Fig 5f) Extracted Tumor using Improved Sobel

Extracted Tumor using Watershed Segmentation Fig 2e) Extracted Tumor using Watershed method Fig 3e) Extracted Tumor using Watershed method Fig 4e) Extracted Tumor using Watershed method Fig 5e) Extracted Tumor using Watershed method

Extracted Tumor using Sobel Method Fig 2d) Extracted Tumor using Sobel Fig 3d) Extracted Tumor using Sobel Fig 4d) Extracted Tumor using Sobel Fig 5d) Extracted Tumor using Sobel

Regions formed using Improved Sobel Method Fig 2c) Regions formed using Improved Sobel Method Fig 3c) Regions formed using Improved Sobel Fig 4c) Regions formed using Improved Sobel Fig 5c) Regions formed using Improved Sobel

Regions formed using Watershed Segmentation Method Fig 2b) Regions formed using Watershed Segmentation Method Fig 3b) Regions formed using Watershed Segmentation Method Fig 4b) Regions formed using Watershed Segmentation Method Fig 5b) Regions formed using Watershed Segmentation Method

Regions formed using Sobel Method Fig 2a) Regions formed using Sobel Method Fig 3a) Regions formed using Sobel Method Fig 4a) Regions formed using Sobel Method Fig 5a) Regions formed using Sobel Method

Original Image Fig 2) Tumor 2 Fig 3) Tumor 3 Fig 4) Tumor 4 Fig 5) Tumor 5

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tumor is detected, but in Fig 4e) the watershed method of segmentation gives too many closed contours because it is over sensitive method. Therefore watershed method gives small size tumor detection. And in Fig 4f) the improved sobel segmentation gives closed contours with proper boundaries for the tumor detected. Hence the tumor extracted in improved sobel method is close to the original tumor.

Similarly in other tumor images shown above, improved sobel method gives better tumor but slightly shrunk in size compared to th extracted tumor using sobel segmentation and watershed segmentation methods. The edges detected by watershed method have too many closed contours whereas edges detected using sobel method gives open contours. This makes both the methods difficult in finding the exact and correction location of tumor. However by applying improved edge detection algorithms does not always lead to closed contours in intermediate steps, but gives closed contour near the brain tumor region. Hence improved sobel segmentation method is able to find correct tumor which is more close to tumor in original image compared to tumors detected in other methods.

For comparing Improved sobel method of segmentation with watershed segmentation and Sobel method three parameters are considered.

1. Entrophy:

In general all images will have grey levels [1, 2 ...Lu] spatially distributed, where Lu is the Luminance. Entrophy works on a segmented image with n regions where Vj is the luminance in a region and Lm (Rj) is the total number of

Pixels in a region Rj, where m is the Luminance value in it.

Let Here and Hlay are two parameters which check the uniformity in a segmented image [25]. Here be the parameter which maximizes the uniformity among the pixels in a segmented region and Hlay

be the parameter which checks the layout in a segmented image and it maximizes the differences between different regions in a segmented image.

The entrophy is given by

Q = H ere(I) + H lay(I)

2. Correlation: Let S(x) and T(x) denotes the source and target images respectively, where x is the coordinate‟s vector of the image. The deformed source image or the output image is O(x). Hence O(x) = S (T(x)). Now the images O(x) and T(x) are the statistical vectors. The correlation between these two vectors is given by

C = E [O(x) T(x)], where E is the expectation operator.

3. Energy: Energy based active contours are classified as geometric or parametric. Parametric curves are used to draw the contour in parametric method whereas it requires constant curves to represent the boundaries in an image. This is done by minimizing energy function in parametric contour method.

We have internal and external energy minimizing functions which are also called internal and external forces [26]. The internal energy minimizing function concentrates on the geometric curves for indicating boundaries in an image and

the external minimizing function concentrates on all other forces which will guide the curve to delineate in reaching the desired contour.

Fig 6. Entropy for Tumor 1-4

Fig 7. Correlation for Tumor 1-4

Fig 8. Energy for Tumor 1-4

It can be seen in Fig 6. That entrophy in improved sobel method is lower compared to watershed segmentation and sobel method segmentation in most of the cases. In Fig 7.

Correlation is also less in improved sobel segmentation, whereas energy is less in improved sobel method compared to sobel segmentation (in Fig 8.).

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V. CONCLUSION

In our work, we have applied improved sobel segmentation on an MRI brain tumor image. This method gives less false edges and better closed contours with proper boundaries.

Hence tumor can be detected more accurately compared to sobel segmentation and watershed segmentation. The tumor detected in proposed method is close to the tumor in original image compared to extracted tumor in other two methods. In future edge detection can be done with still improved closed contour algorithms which will increase the area of the region as well as decrease their thickness of boundaries.

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