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    34 107-1-pb 34 107-1-pb Document Transcript

    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 Brain Segmentation using Fuzzy C means clustering to detect tumour Region. Prof. A.S.Bhide1 Priyanka Patil2, Shraddha Dhande3 1 2 Electronics and Communication Engineering, Electronics and Communication Engineering, North Maharashtra University, Jalgaon, India. North Maharashtra University, Jalgaon, India 3 Electronics and Communication Engineering, Vishwakarma Institute of Technology, Pune, IndiaAbstract There are two classifications exist to recognize a pattern, and Tumor segmentation from MRI data is an they are supervised classification and unsupervisedimportant but time consuming manual task performed by classification. A commonly used unsupervised classificationmedical experts. The research which addresses the method is a Fuzzy C Means algorithm [2].diseases of the brain in the field of the vision by computeris one of the challenges in recent times in medicine, the Clustering is a process of partitioning or grouping a givenengineers and researchers recently launched challenges tocarry out innovations of technology pointed in imagery. sector unlabeled pattern into a number of clusters such thatThis paper focuses on a new algorithm for brain similar patterns are assigned to a group, which is consideredsegmentation of MRI images by fuzzy c means algorithm to as a cluster. There are two main approaches to clusteringdiagnose accurately the region of cancer. In the first step it which is crisp clustering and fuzzy clustering techniques .Oneproceeds by noise filtering later applying FCM algorithm to of the characteristic of crisp clustering method is that thesegment only tumor area .In this research multiple MRI images boundary between clusters is fully defined but in many realof brain can be applied detection of glioma (Tumor) growth by cases the boundary between clusters cannot be clearly defined.advanced diameter technique. Some patterns may belong to more than one cluster. In such cases, the fuzzy clustering method provides a better and moreIndex Terms - Brain tumor, MRI, Imaging, Segmentation. useful method to classify these patterns. Fuzzy clustering method and its derivatives have been used for pattern 1. INTRODUCTION recognition, classification, data mining, and image segmentation It has also been used for medical image data Brain tumor is an abnormal mass of tissue in which cells analysis and modelling etc. Clustering is used for patterngrow and multiply uncontrollably, seemingly unchecked by recognition in image processing, and usually requires a highthe mechanisms that control normal cells. Brain tumours can volume of computation. This high volume computationbe primary or metastatic, and either malignant or benign. A requires considerable amount of memory which may lead tometastatic brain tumor is a cancer that has spread from frequent disk access, making the process inefficient. With theelsewhere in the body to the brain [3]. development of affordable high performance parallel systems, Magnetic Resonance Imaging (MRI) is an advanced medical parallel algorithms may be utilized to improve performanceimaging technique used to produce high quality images of the and efficiency of such tasks. The computation speed andparts contained in the human body MRI imaging is often used memory requirement needed for executing FCM is a bigwhen treating brain tumours, ankle, and foot. From these hurdle which tried to overcome in this report. In FCM, thehigh-resolution images, we can derive detailed anatomical cluster centre initialized by random numbers and it requiresinformation to examine human brain development and more number of iteration for converging to a final actualdiscover abnormalities. Nowadays there are several cluster centre [2].methodology for classifying MR images, which are fuzzy The tumor volume is prognostic factor in the treatment ofmethods, neural networks, atlas methods, knowledge based malignant tumours. Manual segmentation of brain tumourstechniques, shape methods, variation segmentation. Image from MR images is a challenging and time consuming task. Insegmentation is the primary step in image analysis, which is this study a new approach has been discussed to detect theused to separate the input image into meaningful regions. 85 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012volume of brain tumor using diameter and graph based image sharpening is to make the tumor edges, contour linesmethod to find the volume. Here MRI data set from patients and image details clearer. Same process will be applied to thewere collected. The graph based on pixel value is drawn real target image.taking the various points from the tumor cells lies in theoriginal position from the affected region. Here the affectedregion is considered as ellipse shape and the volumes have 3.1 Database Collectionbeen calculated from it. In this system the mean has beenfound from the volumes grown in the affected region. The 3.1.1 CT Scanexperimental results show that 96% brain tumor growth and CT scans are a specialized type of x-ray. Thevolume can be measured by graph and diameter method [6]. patient lies down on a couch which slides into a large circular opening. The x-ray tube rotates around the patient and a 2. LITERATURE SURVEY computer collects the results. These results are translated intoThe previous methods for brain tumor segmentation are images that look like a "slice" of the person.thresholding, region growing & clustering.Thresholding is the simplest method of image segmentation. Sometimes a radiologist will decide that contrast agentsFrom a greyscale image, thresholding can be used to should be used. Contrast agents are iodine based and arecreate binary images. During the thresholding process, absorbed by abnormal tissues. They make it easier for theindividual pixels in an image are marked as "object" pixels if doctor to see tumors within the brain tissue. There are sometheir value is greater than some threshold value (assuming an (rare) risks associated with contrast agents and you shouldobject to be brighter than the background) and as make sure that you discuss this with the doctor before arriving"background" pixels otherwise. This convention is known for the examination.as threshold above. Variants include threshold below, which isopposite of threshold above; threshold inside, where a pixel is CT is very good for imaging bone structures. In fact, itslabelled "object" if its value is between two thresholds; and usually the imaging mode of choice when looking at the innerthreshold outside, which is the opposite of threshold inside. ears. It can easily detect tumours within the auditory canalsTypically, an object pixel is given a value of “1” while a and can demonstrate the entire cochlea on most patients.background pixel is given a value of “0.” Finally, a binaryimage is created by colouring each pixel white or black,depending on a pixels labels. 3.2.2 MRIThe major drawback to threshold-based approaches is that MRI is a completely different. Unlike CT itthey often lack the sensitivity and specificity needed for uses magnets and radio waves to create the images. No x-raysaccurate classification. are used in an MRI scanner. The patient lies on a couch thatThe first step in region growing is to select a set of seed looks very similar the ones used for CT. They are then placedpoints. Seed point selection is based on some user criterion in a very long cylinder and asked to remain perfectly(for example, pixels in a certain gray-level range, pixels still. The machine will produce a lot of noise andevenly spaced on a grid, etc.). The initial region begins as the examinations typically run about 30 minutes.exact location of these seeds. The cylinder that you are lying in is actually a very largeThe regions are then grown from these seed points to adjacent magnet. The computer will send radio waves through yourpoints depending on a region membership criterion. The body and collect the signal that is emitted from the hydrogencriterion could be, for example, pixel intensity, gray level atoms in your cells. This information is collected by antexture or colour. antenna and fed into a sophisticated computer that producesSince the regions are grown on the basis of the criterion, the the images. These images look similar to a CAT scan but theyimage information itself is important. For example, if the have much higher detail in the soft tissues. Unfortunately,criterion were a pixel intensity threshold value, knowledge of MRI does not do a very good job with bones.the histogram of the image would be of use, as one could use One of the great advantages of MRI is the ability to changeit to determine a suitable threshold value for the region the contrast of the images. Small changes in the radio wavesmembership criterion and the magnetic fields can completely change the contrast of the image. Different contrast settings will highlight different types of tissue. 3. BRAIN MRI IMAGE PREPROCESSING Another advantage of MRI is the ability to change the imaging plane without moving the patient. If you look at the images to In order to improve the visual effects of the image the left you should notice that they look very different. Thefor further image recognition, MRI image pre-processing is top two images are what we call axial images. This is whatneeded, mainly including colour image greyscale, image you would see if you cut the patient in half and looked at themsmoothing and sharpening and so on. Image smoothing is to from the top. The image on the bottom is a coronaleliminate noise and improve image quality. The purpose of image. This slices the patient from front to back. Most MRI 86 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012machines can produce images in any plane. CT can not do sensor accuracy typically 10 or 12 bits per sample and tothis. guard against round off errors in computations. Sixteen bitsContrast agents are also used in MRI but they are not made of per sample (65,536 levels) is a convenient choice for suchiodine. There are fewer documented cases of reactions to uses, as computers manage 16-bit words efficiently. The TIFFMRI contrast and it is considered to be safer than x-ray and the PNG among other image file formats supports 16-bitdye. Once again, you should discuss contrast agents with your grayscale natively, although browsers and many imagingphysician before you arrive for the examination. programs tend to ignore the low order 8 bits of each pixel. No matter what pixel depth is used, the binary3.2 Grayscale representations assume that 0 is black and the maximum value In photography and computing, a grayscale or 255 at 8 bpp, 65,535 at 16 bpp, etc. is white, if not otherwisegrayscale digital image is an image in which the value of each noted [1].pixel is a single sample, that is, it carries only intensityinformation. Images of this sort, also known as black-and- 3.3 Converting Color to Grayscalewhite, are composed exclusively of shades of gray, varying Conversion of a color image to grayscale is notfrom black at the weakest intensity to white at the strongest. unique; different weighting of the color channels effectively Grayscale images are distinct from one-bit black- represents the effect of shooting black-and-white film withand-white images, which in the context of computer imaging different-colored photographic filters on the cameras. Aare images with only the two colors, black, and white also common strategy is to match the luminance of the grayscalecalled bi-level or binary images. Grayscale images have many image to the luminance of the color image [1].shades of gray in between. Grayscale images are also called To convert any color to a grayscale representation ofmonochromatic, denoting the absence of any chromatic its luminance, first one must obtain the values of its red,variation. green, and blue (RGB) primaries in linear intensity encoding, Grayscale images are often the result of measuring by gamma expansion. Then, add together 30% of the redthe intensity of light at each pixel in a single band of the value, 59% of the green value, and 11% of the blue valueelectromagnetic spectrum e.g. infrared, visible light, these weights depend on the exact choice of the RGBultraviolet, etc, and in such cases they are monochromatic primaries, but are typical. Regardless of the scale employedproper when only a given frequency is captured. But also they 0.0 to 1.0, 0 to 255, 0% to 100%, etc., the resultant number iscan be synthesized from a full color image. the desired linear luminance value; it typically needs to be The intensity of a pixel is expressed within a given gamma compressed to get back to a conventional grayscalerange between a minimum and a maximum, inclusive. This representation [1].range is represented in an abstract way as a range from 0 This is not the method used to obtain the luma in themeans total absence, black and 1 means total presence, white YUV and related color models, used in standard color TV andwith any fractional values in between. video systems as PAL and NTSC, as well as in the L*a*b Another convention is to employ percentages, so the color model. These systems directly compute a gamma-scale is then from 0% to 100%. This is used for a more compressed luma as a linear combination of gamma-intuitive approach, but if only integer values are used, the compressed primary intensities, rather than use linearizationrange encompasses a total of only 101 intensities, which are via gamma expansion and compression [1].insufficient to represent a broad gradient of grays. Also, the To convert a gray intensity value to RGB, simply setpercentile notation is used in printing to denote how much ink all the three primary color components red, green and blue tois employed in half toning, but then the scale is reversed, the gray value, correcting to a different gamma if necessary.being 0% the paper white or no ink and 100% a solid black orfull ink. 3.4 Filtering an Image In computing, although the grayscale can be Image filtering is useful for many applications,computed through rational numbers, image pixels are stored in including smoothing, sharpening, removing noise, and edgebinary, quantized form. Some early grayscale monitors can detection. A filter is defined by a kernel, which is a smallonly show up to sixteen (4-bit) different shades, but today array applied to each pixel and its neighbors within an image.grayscale images as photographs intended for visual display In most applications, the center of the kernel is aligned withboth on screen and printed are commonly stored with 8 bits the current pixel, and is a square with an odd number 3, 5, 7,per sampled pixel, which allows 256 different intensities i.e., etc. of elements in each dimension. The process used to applyshades of gray to be recorded, typically on a non-linear scale. filters to an image is known as convolution, and may beThe precision provided by this format is barely sufficient to applied in either the spatial or frequency domain [1].avoid visible banding artifacts, but very convenient for Within the spatial domain, the first part of theprogramming due to the fact that a single pixel then occupies convolution process multiplies the elements of the kernel bya single byte. the matching pixel values when the kernel is centered over aTechnical uses in medical imaging or remote sensing pixel. The elements of the resulting array which is the sameapplications often require more levels, to make full use of the size as the kernel are averaged, and the original pixel value is 87 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012replaced with this result. The CONVOL function performs centroid or prototypes. Many techniques have been developedthis convolution process for an entire image. Within the for clustering data. In this report c-means clustering is used.frequency domain, convolution can be performed by It’s a simple unsupervised learning method which can be usedmultiplying the FFT of the image by the FFT of the kernel, for data grouping or classification when the number of theand then transforming back into the spatial domain. The clusters is known. It consists of the following steps.kernel is padded with zero values to enlarge it to the same sizeas the image before the forward FFT is applied. These types of Step 1:filters are usually specified within the frequency domain anddo not need to be transformed. IDLs DIST and HANNING Choose the number of clusters - Kfunctions are examples of filters already transformed into thefrequency domain [1]. Step 2: Since filters are the building blocks of many image Set initial centers of clusters c1, c2… ck;processing methods, these examples merely show how toapply filters, as opposed to showing how a specific filter may Step 3:be used to enhance a specific image or extract a specificshape. This basic introduction provides the information Classify each vectornecessary to accomplish more advanced image-specificprocessing. filters can be used to compute the first derivatives x [x , x ,....x ] T into the closest centre ci byof an image [1]. Euclidean distance measure3.5 Median filter In image processing, it is often desirable to be able to ||xi-ci ||=min || xi -ci||perform some kind of noise reduction on an image or signal.The median filter is a nonlinear digital filtering technique, Step 4:often used to remove noise. Such noise reduction is a typicalpre-processing step to improve the results of later processing Recomputed the estimates for the cluster centers ci(for example, edge detection on an image). Median filtering is Let ci = [ci1 ,ci2 ,....cin ] Tvery widely used in digital image processing because, undercertain conditions, it preserves edges while removing noise cim be computed by, cim = ∑xli ∈ Cluster(Ixlim) 4. FUZZY C MEANS ALGORITHM NiThe goal of a clustering analysis is to divide a given set ofdata or objects into a cluster, which represents subsets or a Where, Ni is the number of vectors in the i-th cluster.group. The partition should have two properties: Step 5:1. Homogeneity inside clusters: the data, which belongs to onecluster, should be as similar as possible. If none of the cluster centers (ci =1, 2,…, k) changes in step 4 stop; otherwise go to step 3.2. Heterogeneity between the clusters: the data, whichbelongs to different clusters, should be as different as 5. IMPLEMENTATION OF FCMpossible. The original MRI image of brain is as followsThe membership functions do not reflect the actual datadistribution in the input and the output spaces. They may notbe suitable for fuzzy pattern recognition. To buildmembership functions from the data available, a clusteringtechnique may be used to partition the data, and then producemembership functions from the resulting clustering. Clustering is a process to obtain a partition P of a set E of Nobjects Xi (i=1, 2,…, N), using the resemblance ordissemblance measure, such as a distance measure d. Apartition P is a set of disjoint subsets of E and the element Psof P is called cluster and the centers of the clusters are called 88 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 structures such as white and gray matter as well as their large variability. The filtered image is converted to grey in preprocessing and FCM is applied to it gives the segmented tumor.Fig 1.origional MRIOriginal MRI image is filtered by using median filter. Thefiltered image is shown below. Fig.3 segmented tumor. 6. DIAMETER METHOD FOR VOLUME CALCULATION Diameters of tumor were manually measured on MRI films with callipers. In each case where a second lesion was present, or the shape of the lesion was best characterized by two ellipsoids, a second set of three diameters was also recorded, and the volumes were summed. If one or more necrotic or cystic areas were thought to be present, additional diameters for the cystic component were recorded, and the computed cystic volume was Subtracted from the overall volume Readers were not aware that an end point of this study was the determination of how many sets of diameters (one v two vFig.2 filtered image three diameter measurements) were thought to be required to accurately characterize the lesion volume [6]. The formulaThe algorithm of fuzzy c-means (fuzzy c-means) is a used to compute volumes was the standard volume of anclassification algorithm based on fuzzy optimization of a ellipsoid, as follows:quadratic criterion of classification where each class isrepresented by its center of gravity.The algorithm requires knowing the number of classes in V = 4/3 pi (a *b*c*) …….. (1)advance and generates classes through an iterative processminimizing an objective function. Thus, it provides a fuzzy Where a, b, and c are the three radii (half the diameters). Inpartition of the image by giving each pixel a degree of addition to the total volume, the individual diameters werebelonging to a given region. also recorded to allow analysis on a single- or dual-diameterSegmentation of anatomical structures is a critical task in basis, ie, diameter or area rather than a volume estimate.medical image processing, with a large range of applicationsgoing from visualization to diagnosis.For example, to delineate structures in the mid-sagittal planeof the brain in the context of a pre-operative planning, anaccurate segmentation of the hemispheres, and especially oftheir internal faces, is needed. In such a task, the maindifficulties are the non-homogeneous intensities within thesame class of tissue, and the high complexity of anatomical 89 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering Volume 1, Issue 2, April 2012 on a criterion introduced for fuzzy regions. The main advantage of this method is that, first it requires no prior information on the images to segment. The computation speed is fast so less execution time. Less memory requirement.MRI images are highly weighted so other methods consume high iteration and also more sensitive to the initialization of cluster centres but using FCM less iterations are needed in clusteringFig.4.Diameter Method Tumor volume is an important diagnostic indicator in treatment planning and results assessment for brain tumor.In this diameter method the volume has been calculated using The measurement of brain tumor volume can assist tumorformula 1. Volume calculation has been done in a step by step staging for brain tumor volume measurements is developedprocess from our input MRI image; the affected region has which overcome the problem of inter-operator variance,been changed as ellipse or circle shape. In formula 1 the besides partial volume effects and shows satisfactoryvolume has been found from MRI data set using different performance for segmentation. This method is applied to 8-parameters (a, b, c). to find out the volume from MRI data set- tumor contained MRI slices from 2 brain tumor patients_ data1. This result will produce out the volume of brain tumor. sets of different tumor type and shape, and betterAgain this same diameter method has been used and has found segmentation results are achieved. In this paper a newout the volume -2 up to N times. N is a number of steps to approach has been discussed to detect the volume of braindetermine the volume by different parameters as shown in fig. tumor using diameter and graph based method to find theThe mean has been measured from these volumes using volume.formula-3 which is equal to average of volumes calculatedusing different parameters. From this mean the volume of Referencesglioma has been determined from day by day MRI report. The [1] W. Gonzalez, “Digital Image Processing”, 2nd ed. Prenticegraph shows the brain tumor growth affected in brain cells [6]. Hall, Year of Publication 2008, Page no 378.Suppose if we have 5 MRI images of brain if these MRI are [2] S. Murugavalli, V. Rajamani, “A high speed parallel fuzzyapplied to FCM algorithm then it will segment all the tumor c-mean algorithm for brain tumour segmentation”,” BIMEregion and calculates the area and volume of individual MRI. Journal”, Vol. no: 06, Issue (1), Dec., 2006If the tumor is growing day by day then it will plot then tumorgrowth as shown in fig.5 [3]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. [4] 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 [5] Matei Mancas, Bernard Gosselin, Benoît macq, “Segmentation Using a Region Growing Thresholding” [6] S.Karpagam and S. Gowri “Detection of Glioma (Tumor) Growth by Advanced Diameter Technique Using MRI Data”Fig.5 tumor growthCONCLUSIONWe have presented a new method for Brain Segmentation tolocalize Tumour. The method of segmentation of colourimages is based on fuzzy classification. It uses a fine initialsegmentation obtained by applying the FCM algorithm based 90 All Rights Reserved © 2012 IJARCSEE