Proceedings of the 8th INDIACom; INDIACom-2014 2014 International Conference on “Computing for Sustainable Global Development”, 5 th– 7th March, 2014 Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA)
Effective Evaluation of Tumour Region in Brain MR Images Using Hybrid Segmentation Kimmi Verma1, Shabana Urooj2 and Rituvijay3 Abstract - The aim of this work is to validate a quantitative technique to extract different attributes from MR images. A method known as hybrid segmentation that combines threshold segmentation, watershed segmentation, edge detection and morphological operators is considered jointly. This combined technique is experimented with MR scanned images of human brains to detect tumour. Exact size and location of tumour is detected using present hybrid segmentation technique. Index Terms – hybrid segmentation,edge morphological operators, watershed segmentation
detection,
I. INTRODUCTION Exact measurements in brain diagnosis are difficult because of various shapes, sizes and appearances of tumours. Tumours also cause abrupt defects in nearby tissues. Tumour is an abnormal growth of body tissue, it can be cancerous (malignant) or non-cancerous (Benign). In medical imaging technique, MRI Magnetic resonance imaging technique is used in radiology to visualize the internal structures of the body in detail. This produces a rotating magnetic field detectable by the scanner and this information gets recorded and constructs an image of the scanned area of the body. MRI has excellent contrast within soft tissues. Accurate anatomical three dimensional (3D) models are derived from 2D MRI medical image data which helps in providing precise and accurate diagnostic information about spatial relationships between critical anatomical structures such as critical anatomical structures such as eloquent cortical areas, vascular structures etc and other pathological findings which were otherwise indistinguishable by the naked eye. Medical images are obtained for various applications which includes image guided surgery, surgical simulation etc. When working with medical images, i.e. Magnetic Resonance Imaging (MRI), X-Ray, ultrasound and CT images etc, it is often to delineate the areas and volumes of interest [14]. 1
Assistant Professor, Dept of Instrumentation & Control Engineering, Galgotias College of Engineering & Technology, Greater Noida, India. 2 Assistant Professor, Electrical Engineering. Dept., School of Engineering, Gautam Buddha University, Greater Noida UP 3 Reader, Department of Electronics, Banasthali Vidyapith, Rajasthan India E-mail:
[email protected],
[email protected] 3
[email protected]
Medical experts face the task of extracting abnormalities within such images. Hence, there is a requirement of robust methods to process the interpretation of huge amounts of data with more accuracy. To overcome these hindrances in clinical diagnosis, segmentation of medical images provides the methods for increasing the diagnostic accuracy. Segmentation is the identification and isolation of an image into regions like structural units. It is used to differentiate the physiological and biological structures of interest. Segmentation is grouped into three classes’ i.e. regional methods, edge based methods, and pixel based methods [13]. The technique which will be used here for segmentation comes under pixel based segmentation and known as threshold segmentation which classifies each pixel in terms of intensity values and compared with some threshold value. In this paper pixel based segmentation and edge based segmentation method is used together. S Zhu et al [7] paper presents an automatic image segmentation method using thresholding technique but they used an assumption that neighbouring pixels lies within certain range which belong to same class. S. Xavierarockiaraj et al [1] proposed a method based on histogram thresholding which accounts uniform background and irregular objects are placed. Split and merge approaches were used and its performance largely depends on the selected homogeneity criterion [8]. Masayoshi T et al [29] addressed the classical problem of detecting low level structures in images using image segmentation. M Niwu, et al [6] represented that ANNs show a paradigm for machine learning and can be used in a variety of ways for image segmentation. When experts work on tumour images, then they use different algorithms, pixel based, texture based, structure based. Ong. S [13] proposes a novel segmentation scheme for textured grey level and colour images. Bouaynaya N [28] detects the difference between pairs of pixel around a pixel and used the pixel of highest value among four pair of pixels to form a line through the middle pixel. The approach proposed in [31] used a linear combination of Type-0 and Type-II polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation is presented by Bhateja V et al in [32] and [33]. In existing methods, scope of enhancement and amendment is pragmatic. To overcome the different limitations, the hybrid segmentation is done in this work, which observes prominent
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Proceedings of the 8thINDIACom; INDIACom-2014 2014 International Conference on “Computing for Sustainable Global Development”, 5th– 7th March, 2014 and remarkable results. The focus is highlighted to develop an accurate segmentation and edge detection technique with the morphological algorithm of watersheds. Firstly, the preprocessing step is executed to minimise the spatial resolution without losing important image formation, secondly processing is done in which image is divided on the grounds of similar attributes, separated out into groups, ahead post processing techniques are approached which is done using threshold segmentation, watershed segmentation . To make the tumour section more visible some morphological operators are used to differentiate the highest intensity region than other regions of the image. II.METHODOLOGY Magnetic resonance imaging scans of brain tumour were segmented along with edge detection together to extract the tumour exact edges, its types, its shape, its anatomic functional positioning as well as its effect on other brain areas. . A.Image Acquisition MR images are used as input and then the images are stored in MATLAB in .jpg form and converted into a gray scale image. Gray scale image entries differ from low level to 28 value, where low level signifies total black colour and 2 8 signifies pure white colour, therefore entries differs from white to black. For experimental results both male and female patients are examined with MRI scans and their scans are stored in database of images in JPEG format. B. Pre-processing Pre-processing is done to enhance the image, to improve the finer details. Enhancement will result in more well defined edges and a sharpened image is formed. Given image is represented in two dimensional arrays I (p, q). Each element of variable I represents a single pixel. The MRI scanned image is acquired I (p, q) and stored in database is converted to gray scale image of size (28 * 28; 21). 1. Image Sharpening The two dimensional image I (p, q) is sharpened using the techniques of linear filtering. FIR filters are used because of linear phase characteristics. Filtered pixel is determined from a linear combination of surroundings pixels. FIR filter is specified by the impulse response function, filter coefficients g(n). In image sharpening filter function exists in two dimensional x(m, n). These two dimensional filter weights are applied to the I (p, q) image using convolution.
C. Processing The objective of segmentation is to partition an image into regions. In these boundaries centre regions are to be calculated, which is based on discontinuities in intensity levels. It is carried on the grounds of similar attributes separated out into groups. Desired features from the image are extracted through segmentation from which information can easily be obtained. To segment brain tumour from MR image is a remarkable but exigent task in the area of medical imaging. D.Post Processing The following steps are involved in post processing of image 1. Threshold Segmentation It is considered as one of the simplest segmentation technique creating a binary partitioning in accordance with the image intensities, the image is segmented into scalar images. The threshold value is the intensity value and it separates the required classes. It is achieved by clustering all pixels with higher intensity than the threshold value in one unit and all other pixels into another unit. The threshold levels are determined with the help of intensity histograms. The complete image is scanned pixel by pixel and labeling of every pixel is performed as an object pixel individually, the resultant background pixel depends on the value of object pixel. 2. Watershed Segmentation Algorithm The pixels of an image based on their intensities are grouped in this technique. Watershed transform used for segmentation helps to calculate catchment basins along with watershed rigid lines of the threshold segmented image [20]. Considering input image as a base surface and its light pixels are labeled high and dark pixels are labeled low. Below given steps are followed for the calculation of watershed segmentation[21]: 1. To compute Segmentation function for n points, assume Y[n]. Let Geometrically, Y[n] is the set of coordinates of points in x (a, b) lying below the plane x (a, b) = h 2. Let C[n] shows the union of the flooded catchment basins at stage n
And Gives union of all
Thus the image array is convolved with a set of filter coefficients.
3.
Segmentation function is modified so that only minima are at the foreground and background of the image at particular location.
Copy Right © INDIACom-2014; ISSN 0973-7529; ISBN 978-93-80544-11-3
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Effective Evaluation of Tumour Region in Brain MR Images Using Hybrid Segmentation
3. Morphological Operators The morphology is based on set theory. Here, the set of all white pixels in a binary image is a complete morphological description of the image. The image is converted in the binary format and the morphological operators are applied. The objective of the morphological operators is to divide the tumour part from the image. The tumour region has the highest intensity than other regions of the image, this differentiates tumour from the image and tumour becomes visible in white spot using morphological operators. Using morphological operators, a morphological structure of the element is designed to set reflection and translation where reflection of set A, denoted as A is given by Where A is the set of pixels, two dimensional points representing the image and is set of points in A whose (x, y) coordinates are replaced by (-x, -y) and translation of a set a by appoint C = (C1, C2), denoted ;
basically the region with the intensity values greater than the defined threshold. High intensity areas mostly consist of tumours. So through threshold segmentation location of tumour can be specified.
Figure2: Threshold Segmentation Then watershed segmentation is applied on the resultant image obtained after threshold segmentation. Only the portion which contains tumour is highlighted in Fig. 3.The white portion is marked by using watershed segmentation method.
Where A is the set of pixels representing an object in image then is the set of points in A, whose (x, y) coordinates have been replaced by (x + c1, y + c2). Other operations performed on an image are erosion and dilation. Where erosion of A and B denoted by B where A and B are sets, = Which shows erosion of A by B is set of all points z such that B, translated by Z, is in A. On the other hand dilation is given by; Where B is defined as the structuring element here and A is the set of image which is dilated. Erosion is a shrinking operation and dilation grows or thickens objects in a binary image and thickening is controlled by the shape of the structuring element.
Figure3: Watershed Segmentation
III.RESULTS An input image is shown in Fig. 1, which shows a tumours brain.
Figure4: Morphological Operators Eroded
Figure.1 Input Image (Courtesy [30]) Threshold segmentation is applied on the input image which contains brain tumour and the obtained results are shown in the Fig. 2. A white spot is shown in this figure, which is the result of threshold segmentation applied on the images. This is Copy Right © INDIACom-2014; ISSN 0973-7529; ISBN 978-93-80544-11-3
Figure 5: Eroded Image 651
Proceedings of the 8thINDIACom; INDIACom-2014 2014 International Conference on “Computing for Sustainable Global Development”, 5th– 7th March, 2014 Thereafter, morphological operators are applied on the image. The results are quite efficient and this technique leads to prominent result. Fig. 5 shows the resultant image obtained after applying erosion functions. Tumour is tinted as white portion after applying hybrid segmenting techniques in Fig. 6.
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[10]. Figure6: Output Image IV. CONCLUSION This work is carried out to detect brain tumour using image processing techniques. The main method used is segmentation, which is done using a technique based on threshold segmentation, watershed segmentation and morphological operators together. The proposed segmentation technique was experimented with MRI scanned images of human brains; thus detecting tumour in the images. Scanned images of human brains were taken, using MRI process and then were processed through segmentation techniques. Various images were subjected to the experiment and hence proficient results are obtained in the present approach.
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