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International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 3, Issue 4, Oct 2013, 87-94 © TJPRC Pvt. Ltd.

ADVANCE FEATURE EXTRACTION OF MRI BRAIN IMAGE AND DETECTION USING LOCAL SEGMENTATION METHOD WITH WATERSHED S. SIVAPERUMAL1, & M. SUNDHARARAJAN2 1

Assistant Professor (SG) / ECE, Saraswathi Velu College of Engineering, Sholinghur, Tamil Nadu, India 2

Principal, Sri Lakshi Ammal Engineering College, Selaiyur, Tamil Nadu, India

ABSTRACT Segmentation is the process of separate an observed image into its homogeneous or constituent regions. Image segmentation, feature extraction and detection form a fundamental problem in many applications. The approach used in this work is based on the watershed segmentation. In this paper we proposed a regional contrast based saliency extraction algorithm to detect the tumours. Watershed segmentation classifies pixels into regions using gradient descent on image features and analysis of weak points along region boundaries. The principle of this method states that the step is processing a pixel to segment the local region encompassing that pixel. This provides a snapshot of the local structural features of the image with the signal clearly separated from the noise. The lower level is local segmentation which operates in a purely local manner using only a small number of pixels. The higher level is global segmentation which attempts a group together related pixels throughout the image. The highest level is object recognition, whereas global segments are combined into logical units representing real world objects of interest. It gives higher precision and better recall rates.

KEYWORDS: Image Segmentation, Mathematical Morphology, Topological Asymptotic Expansion, Topological Gradient, Watershed Transformation

INTRODUCTION Tumour is defined as the abnormal growth of the tissues. Brain tumour is an abnormal mass of tissue in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells. Brain tumours can be primary or metastatic, and either malignant or benign. A metastatic brain tumour is a cancer that has spread from elsewhere in the body to the brain MRI brain tumour segmentation provides useful information for medical diagnosis and surgical planning [1]. However, it is a difficult task due to the large variance and complexity of tumor characteristics in images, such as sizes, shapes, locations and intensities. The image is described as homogeneous areas according to one or several a priori attributes [2]. In the literature, we can find various segmentation algorithms [3]. Pre-processing of MRI images is the primary step in image analysis which perform image enhancement and noise reduction techniques which are used to enhance the image quality, then some morphological operations are applied to detect the tumor in the image [4] [5]. The morphological operations are basically applied on some assumptions about the size and shape of the tumour and in the end the tumour is mapped onto the original gray scale image with 255 intensity to make visible the tumour in the image [6]. The purpose of this work is to adapt a new method for image segmentation using watershed transformation. This technique is well known to be a very powerful segmentation tool. Gray level images are considered as topographic reliefs, each relief is flooded from its minima and when two lakes merge, a dam is built: the set of all dams define so it is called watershed. Such representation of the watershed simulates the flooding process [8]. Other processes can be found in the literature; particularly efficient algorithms for computing watersheds are described in Beucher (1990) and Soille (1992). One of the advantages of the watershed transformation is that it always provides closed


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contours, which is very useful in image segmentation [11]. Another advantage is that the watershed transformation requires low computation times in comparison with other segmentation methods [13] [14]. The best approach to image segmentation may vary between different applications. The choice between manual, semiautomatic or fully automatic methods depends on the quality of the images, the number of objects needs to be segmented, the amount of available user time, and the required accuracy of the segmentation [16]. The segmentation process is usually based on gray level intensity, colour, shape or texture. Texture can be characterized by local variations of pixel values that repeat in a regular or random pattern on the object or image. It can also be defined as a repetitive arrangement of patterns over a region [18]. Watershed techniques produce a hierarchy of segmentations, thus the resulting segmentation has to be selected using either some apiary knowledge or manually. These methods are well suited for different measurements fusion and they are less sensitive to user defined thresholds [20] [21].

RESEARCH METHODOLOGY The Watershed Transformation A grey-level image may be seen as a topographic relief, where the grey level of a pixel is interpreted as its altitude in the relief. A drop of water falling on a topographic relief flows along a path to finally reach a local minimum. Intuitively, the watershed of a relief correspond to the limits of the adjacent catchment basins of the drops of water. In image processing, different watershed lines may be computed. In graphs, some may be defined on the nodes, on the edges, or hybrid lines on both nodes and edges. Watersheds may also be defined in the continuous domain. There are also many different algorithms to compute watersheds. One aim of this work is to show how the use of mathematical morphology operators can be very useful in image segmentation. Particularly, we show how the watershed transformation contributes to improve the numerical results for image segmentation problems. We describe briefly in this section the basic notions and operators we use. Let u(x, y) with (x, y) ∈ R 2, be a scalar function describing an image I. The morphological gradient of I is defined in Beucher et al. (1993) by

Where (u ⊕D) and (u ⊖D) are respectively the elementary dilation and erosion of u by the structuring element D. The morphological Laplacian is given by

We note here that this morphological Laplacian allows us to distinguish influence zones of minima and suprema: regions with ∆ u < 0 are considered as influence zones of suprema, while regions with ∆D u > 0 are influence zones of minima. Then ∆ u = 0 allows us to interpret edge locations, and will represent an essential property for the construction of morphological filters. The basic idea is to apply either a dilation or an erosion to the image I, depending on whether the pixel is located within the influence zone of a minimum or a maximum. Brain Image Segmentation The systems combine several software packages as building blocks to reach the goal of graph cuts based image segmentation. The systems have been investigated to apply the min-cut/max-flow algorithm in segmenting MRI brain image. The steps which are followed for the tests are: pre-processing, edge detection and boundaries selection, histogram thresholding and segmentation. A block diagram is shown representing the steps in Figure 1.


Advance Feature Extraction of MRI Brain Image and Detection Using Local Segmentation Method with Watershed

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Figure 1: Block Diagram of MRI Brain Image Segmentation

Figure 2: Block Diagram of Proposed Method An MRI brain image is chosen from the database of brain images to be pre-processed. Soft brain tissues such as WM, GM and CSF are surrounded by outward bone structure. A slice is selected on the brain image with bones using MRIcroN software. The segmentation accuracy depends on the slice selection; manual process is followed to select a slice of the displayed brain image (Figure 1). Finally, the selected slice is converted into two dimensional image format using MATLAB code. It is important to extract the internal part of the tissues from the brain MRI for experiment results. Hence the FSL software [22, 23] is used to eliminate outward bone rings and the WM, GM and CSF are remained intact in the obtained brain MR image. An MRI brain image is chosen from the database of brain images to be pre-processed. A slice is selected on the FSL extracted brain image (without bones) using MRIcroN software. The segmentation accuracy depends on the slice selection; manual process is followed to select a slice of the displayed brain image (Figure 2). Finally, the selected slice is converted into two dimensional image format using MATLAB code.

SIMULATION RESULTS AND DISCUSSIONS Next figures show the images as an output. i.e grayscale image, high pass filtered image , threshold image, watershed segmented image, Finally input image and extracted tumour from MRI image. For this purpose real time patient data is taken for analysis. As tumour in MRI image have an intensity more than that of its background so it become very easy locate it and extract it from a MRI image.


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Figure 3: Proposed Simulation Output

Figure 4: Original Image with Noise

Figure 6: Contrast Image

Figure 5: Filtered Image Using Median Filter

Figure 7: Edge Detection Using Canny

Figure 6 shows the segmentation result of the two previous images, according to our new algorithm: a coupled method for image segmentation based on a watershed algorithm and the topological gradient approach. The results obtained show clearly the efficiency of the topological gradient for extracting features from complex images and for reducing drastically the over segmentation.


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Advance Feature Extraction of MRI Brain Image and Detection Using Local Segmentation Method with Watershed

Figure 8: Watershed with Gradient

Figure 9: Watershed with Gradient and Marker Controlled Image

Figure 10: Reconstructed Segmentation Image

Figure 11: Final Segmented Tumor Image

Figure 12: Output Waveform

The watershed transform requires to consider a gray-scale image as a topological surface, where the values of f(x, y) are interpreted as heights. There are the problems to identify the watershed ridge lines on the basis in case that the values of f(x, y) of the diďŹ&#x20AC;erent image regions have the similar heights. So the watershed ridge lines are not detected. This is in the case of a simulated image in Figure 12. On the other hand, too many regions with diďŹ&#x20AC;erent values of f(x, y) result in over-segmentation presented in Figure12.

CONCLUSIONS We have presented in this work a new approach for the segmentation problem taking advantage of the topological


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gradient approach and the watershed transformation. The numerical results obtained are very promising and the proposed algorithm has many advantages: 

First, the algorithm cost is interesting.

Second, as the topological gradient provides a global analysis of the image then the almost unwanted contours due to the noise added to a given image can be significantly reduced by our approach.

Third, the experimental results show that the over segmentation problem, which usually appears with the watershed technique, can be attenuated, and the segmentation results can be performed using the topological gradient approach.

REFERENCES 1.

Mohamed Lamine Toure, “Advanced Algorithm for Brain Segmentation using Fuzzy to Localize Cancer and Epilepsy Region”, International Conference on Electronics and Information Engineering (ICEIE 2010), Vol. no 2.

2.

Dr.G.Padmavathi, Mr.M.Muthukumar and Mr. Suresh Kumar Thakur, “Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 9, May 2010.

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Wankai Deng , Wei Xiao, Chao Pan, Jianguo Liu Key “MRI brain tumor segmentation based on improved fuzzy c-means” Method. Laboratory of Education Ministry for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial Intelligence SPIE Vol. 7497, 74972N, 2009.

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Jackway PT (1999). Gradient watersheds in morphological scale space. IEEE Trans Image Proc 5:913-21.

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Vincent L, Soille P (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal 13:583-9.

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R.C. Gonzalez, R.E. Woods and S.L.Eddins, “Digital image processing using MATLAB”, Second edition, Gatesmark publishing, USA, 2009.

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B. Peng, L. Zheng and J. Yang, “Iterated Graph Cuts for Image Segmentation”, Asian Conference on Computer Vision (ACCV’09), Xi'an, China, September 23-27, 2009.

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Niessen et al. “Multiscale Segmentation of Volumetric MR Brain Images” published in Signal processing for magnetic resonance imaging and spectroscopy by Marcel Dekker, Inc. 2002.

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M. Sonka, V. Hlavac and R. Boyle, “Image processing, analysis, and machine vision”, Third edition, Thomson, USA, 2008.

10. Boykov, Y., Kolmogorov, V.,” An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision”, IEEE Trans, Pattern Anal. Machine Intell., 26, 1124–1137, 2004. 11. Geman, S., Geman, D., “Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images”, IEEE Trans, Pattern Anal. Machine Intell., 6, 721–741 classic, 1984. 12. H. Ishikawa and D. Geiger, “ Segmentation by grouping junctions”, IEEE Conf. Computer Vision and Pattern Recognition, pages 125–131, 1998.


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13. Y. Boykov and M. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images”, Proceedings of ICCV, 2001. 14. Boykov, Y., Veksler, O., Zabih, R., “Fast approximate energy minimization via graph cuts”, IEEE Trans. Pattern Anal. Machine Intell., 23(11), 1222–1239, 2001. 15. Y. Boykov, O. Veksler, and R. Zabih, “ Markov random fields with efficient approximation”, IEEE Conference on Computer Vision and Pattern Recognition, pages 648–655, 1998. 16. Greig, D., Porteous, B.T., Seheult, A.H., “ Exact maximum a posteriori estimation for binary images”, J. R. Stat. Soc., B 2, 271–279, 1989. 17. V. Kwatra, A. Schdl, I. Essa, G. Turk, and A. Bobick, “Graphcut textures: Image and video syntesis using graph cuts”, ACM Transactions Graphics, Proc. SIGGRAPH, July 2003. 18. H. Ishikawa and D. Geiger, ”Oclusions, discontinuities, and epipolar lines in stereo”, Fifth European Conference on Computer Vision, (ECCV’98), Freiburg, Germany, 2-6 June 1998. 19. S. Roy and I. Cox, ”A maximum-flow formulation of the n-camera stereo correspondence problem”, Int. Conf. On Computer Vision, ICCV’98, Bombay, India, 1998. 20. S. Roy and M.-A. Drouin, “Non-uniform pyramid stereo for large images”, IEEE Workshop on Stereo and MultiBaseline Vision, Kauai, Hawaii, 2001. 21. Kolmogorov, V., Zabih, R., “What energy functions can be minimized via graph cuts?”,IEEE Trans. Pattern Anal. Machine Intell. 26(2), 147–159, 2004.


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