Abstract. 2. Segmentation Techniques. Keywords. 1. Introduction. 3. Threshold based Image Segmentation

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1 International Journal of Advanced Coputer Research (ISSN (print): ISSN (online): ) Volue-3 Nuber- Issue-8 March-03 MRI Brain Iage Segentation based on hresholding G. Evelin Sujji, Y.V.S. Lakshi, G. Wiselin Jiji 3 Lecturer, Departent of Electrical and Electronics, BSF Institute of echnology, Bangalore-63 Manager- IR, C-DO, Bangalore HOD, Coputer Science and Engineering, Dr. SivanthiAditanar College of Engineering, iruchendur- 5 3 evelinsuji@yahoo.co.in lakshi@cdotb.ernet.in jijivevin@yahoo.co.in 3 Abstract Medical Iage processing is one of the ost challenging topics in research field. he ain objective of iage segentation is to extract various features of the iage that are used for analysing, interpretation and understanding of iages. Medical Resonance Iage plays a ajor role in Medical diagnostics. Iage processing in MRI of brain is highly essential due to accurate detection of the type of brain abnorality which can reduce the chance of fatal result. his paper outlines an efficient iage segentation technique that can distinguish the pathological tissues such as edea and tuour fro the noral tissues such as White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). hresholding is sipler and ost coonly used techniques in iage segentation. his technique can be used to detect the contour of the tuour in brain. Keywords Abnorality, accurate, segentation, thresholding, tissues, tuour. Introduction Iage segentation subdivides an iage into its constituent regions or objects. he level to which the subdivision is carried depends on the proble being solved. Segentation of nontrivial iages is one of the ost difficult tasks in iage processing. Segentation accuracy deterines the eventual success or failure of the coputerized analysis procedures []. Segentation algoriths are area oriented instead of pixel-oriented. he result of segentation is the splitting up of the iage into connected areas. Iage segentation is the fundaental step in iage analysis, understanding, and interpretation and recognition tasks. Segentation is the ost iportant 97 step in autoated recognition syste which has nuerous applications in the field of edical iaging, satellite iaging, oveent detection, security, surveillance etc. [].. Segentation echniques Iage Segentation partitions an iage into set of regions. he region represents eaningful areas in an iage or be the set of border pixels grouped into structures such as line segents, edges etc. he segentation has two objectives: (i) to decopose an iage into regions for further analysis, (ii) to perfor a change of representation of an iage for faster analysis []. Different types of segentation techniques are used for segentation. Based on the application, a single or a cobination of segentation techniques can be applied to solve the proble effectively. Segentation algorith is based on the properties of gray level values of pixels. he different types of segentation techniques are: (a) Edge based segentation (b) hreshold Based Segentation (c) Region Based Segentation (d) Clustering (e) Matching. In this paper, we discuss about the different types of threshold based segentation echniques. 3. hreshold based Iage Segentation hresholding techniques identify a region based on the pixels with siilar intensity values. his technique provides boundaries in iages that contain solid objects on a contrast background [3]. hresholding technique gives a binary output iage fro a gray scale iage. his ethod of segentation applies a single fixed criterion to all pixels in the iage siultaneously [3]. 3. Global hresholding

2 Suppose the histogra of an iage f (x, y) is coposed of light objects on a dark background. he pixel intensity levels of the object and the background are grouped into two doinant odes. In global thresholding, a threshold value is selected in such a way that it separates the object and the background. he condition for selecting is given as follows: g(x, y) Equation () has no indication on selecting the threshold value. he threshold separates the object fro the dark background. Any point (x,y) for which f(x, y) is called an object point. After thresholding operation, the iage is segented as follows: ixels labeled corresponds to object whereas pixels labeled 0 corresponds to the background. In global thresholding, the threshold value depends only on gray levels of f(x, y). Global thresholding technique will not produce the desired output when pixels fro different segents overlap in ters of intensities [3]. he overlapping of intensities ay be caused due to (a) noise (b) variation in illuination across the iage. In the first case, iniu-error ethod can be used to estiate the underlying cluster paraeters and the threshold is chosen to iniize the classification error. Variable thresholding technique is used for the latter case. Global thresholding is popular due to siplicity and easy ipleentation [5][6]. () 3. Local hresholding Global thresholding ethod is not suitable whenever the background illuination is uneven. In local thresholding technique, the threshold value depends on gray levels of f(x, y) and soe local iage properties of neighboring pixels such as ean or variance []. he threshold operation with a locally varying threshold function (x, y) is given by Where 0 International Journal of Advanced Coputer Research (ISSN (print): ISSN (online): ) Volue-3 Nuber- Issue-8 March-03 applied to f 0 []. Local thresholding is superior to the global threshold ethod in the case of poorly illuinated iages. if f(x,y) if f(x,y) if f(x,y) (x,y) g(x, y) () 0 if f(x,y) (x,y) (x y) = f 0 (x, y) + 0 (3) f 0 (x, y) is the orphological opening of f, and the constant 0 is the result of function graythresh Adaptive hresholding Adaptive thresholding technique is used when iages are captured under unknown lightning condition and it is required to segent a lighter foreground object fro its background or whenever the background gray level is not constant and object contrast varies within an iage. his technique allows the threshold value to change based on the slowly varying function of position in the iage or on local neighboring hood statistics. hreshold depends on the spatial coordinated (x, y) theselves. 4. hreshold Selection he key paraeter in iage segentation using thresholding technique is the choice of selecting threshold value. In case of anual thresholding ethod, the threshold value can be selected by the user with the help of iage histogra. his ethod is generally accoplished by a tool that allows the user to select the threshold value based on choice. In case of autoatic threshold selection ethod, the value of can be chosen based on histogra, clustering, variance, eans etc. 4. Histogra based hreshold Selection An iage having an object on a contrasting background has a biodal histogra. he two peaks correspond to the relatively large nuber of points inside and outside the object. he valley is coonly used to select the threshold gray level. If the iage containing the object is noisy and degraded due to illuination artifacts the histogra itself will be noisy and will not be sharp. his can introduce error in selecting the threshold value. his effect can be overcoe to soe extent by soothing the histogra using either a convolution filter or the curve-fitting procedure [3]. Histogra based thresholding is applied to obtain all possible unifor regions in the iage [7]. Let and be the gray value of the peaks of the histogra. he threshold value is given by (4) Or ay be the gray level at the iniu between the two peaks.

3 International Journal of Advanced Coputer Research (ISSN (print): ISSN (online): ) Volue-3 Nuber- Issue-8 March-03 in H(u) (5). Copute the noralized histogra of the u [, ] input iage. he coponents of the where H(u) is the histogra value at gray level u histogra is denoted by i = n i / MN, where between and i=0,,, L- and MN = n 0 +n +n + +n L- 4. Iterative based hreshold Selection Iterative ethods give better result when the histogra doesn t clearly define valley point. his ethod doesn t require any specific knowledge about the iage. Iterative ethod has the ability to iprove the anti-noise capability.[4] Gonzalez and Woods [00] describe the following iterative procedure:. Select an initial estiate for the threshold value (). his can be done by selecting the idpoint between the iniu and axiu intensity values in the iage.. Segent the iage using. his will produce two sets of pixels G and G. G contains all pixels with intensity values and G contains pixels values <. 3. Copute average intensity values and for each set of pixels. = average value of G = average value of G 4. Copute new threshold value 5. Repeat steps through 4 until the difference in in successive iteration is saller than a predeterined paraeter. his iterative algorith is a special one diensional case of K-eans clustering that converges at a local iniu. But the ain disadvantage is, a different initial estiate for ay give a different result. 4.3 hreshold Selection based on Otsu s ethod A segent is assued to have relatively hoogeneous gray level values, then a threshold value can be selected in such a way that it iniizes the variance of the gray levels within the segent or can be selected that iniizes the variance between objects and background or a ethod that attepts to optiize within and between segents variance []. his ethod axiizes the between-class variance and is based on coputations perfored on the histogra of an iage. Otsu s algorith is as follows:. Copute the cuulative sus (k), k (k) i 0 i, for k= 0,,,, L- 3. Copute the cuulative eans, k (k) i i 0 i, for k=0,,,,l- 4. Copute the global intensity ean, G L- using i G i 0 i 5. Copute the between-class variance, σ B(k),fork 0,,,...,L where σ B (k) G (k) (k) (k) (k) 6. Obtain the Otsu threshold, k*, as the value of k for which σ B(k) is axiu. If the axiu is not unique, obtain k* by averaging the values of k corresponding to the various axia detected. 7. Obtain the separability easure, η * σ (k*) η(k*) B σ G he ain drawback of Otsu s ethod of threshold selection is that it assues that the histogra is biodal. his ethod fails if two classes are of different sizes and also with variable illuination. 4.4 hreshold Selection based on Clustering In this ethod, gray levels are clustered into object and background. Clustering is done to identify natural grouping of data fro a large data set to produce a concise representation of syste behaviour [8]. K-eans clustering is an efficient ethod of threshold selection. Using this algorith, the iage is divided into k segents using (k-) thresholds and iniizing the total variance within each segent. 99

4 International Journal of Advanced Coputer Research (ISSN (print): ISSN (online): ) Volue-3 Nuber- Issue-8 March-03 he value of k has to be selected initially. he basic algorith is as follows:. Choose k cluster centers, either randoly or based on soe heuristics.. Assign each pixel in the iage to the cluster that iniizes the distance between the pixel and the cluster center. 3. Copute the new cluster center by averaging all the pixels in the cluster. 4. Repeat steps and 3 until convergence is attained i.e. the cluster centers do not ove significantly. In this case, distance is calculated by squaring or finding the absolute difference between a pixel and a cluster centre. he difference is based on the properties of pixels such as colour, intensity, texture etc. his algorith is guaranteed to converge, but it ay not return the optial solution. he quality of the solution depends on the initial set of clusters and the value of k 5. Experiental Results 00 Figure ) Original iage, Figure -7) Results of various segentation echniques MRI brain iage segentation based on thresholding was ipleented using MALAB (R00a). Figure -7 show the experiental output of the given MRI input iage, Figure. he output of the segented iage using Global thresholding is shown in Figure. Matlab built in function is used. hresholding based on Otsu s ethod was ipleented and its output is shown in Figure3. he inbuilt function graythreshold ( ) is used to find the threshold value. Figure 4 shows the output of segentation using iterative ethod. he initial threshold is chosen as = (ax (f) + in (f))/,

5 International Journal of Advanced Coputer Research (ISSN (print): ISSN (online): ) Volue-3 Nuber- Issue-8 March-03 [7]. Khang Siang an, Nor Ashidi Mat Isa, color Iage Segentation using Histogra hresholding-fuzzy C-eans hybrid approach, attern Recognition, vol44,pp -5, 0. [8]. Dr. G. adavathi, M. Muthukuar, Suresh Kuar hakur, Non Linear Iage Segentation using Fuzzy C-eans clustering ethod with thresholding for underwater iages, IJCSI, vol7, pp ,00 where f is the input iage. he new value of is calculated by taking the average of the ean of two segents. It can be observed fro Figure3 and 4 that the difference in threshold value is found to be sall. Figure 5 and 6 shows the output of the segentation in which the threshold is selected anually. his ethod requires prior inforation about the iage. he user can change the value of threshold based on the output. his ethod requires a tool that helps the user to observe the output and alter the threshold value. Figure 7 shows the output of histogra of the original iage. he iage can be segented by keeping threshold in the valley. After exaining different inputs, it is observed that the threshold selection based on histogra does not work well for an iage without having obvious peaks and also for the iages with flat and broad valley. G. Evelin Suji is working as a Lecturer in B.S.F. (Border Security Force) Institute of echnology, Bangalore. Her area of interest is edical iaging. She has published research articles in International journals. 6. Conclusions An iage segentation approach based on thresholding has been discussed. his approach for segentation of MRI brain iages can help in the proper detection of the region of interest. he ain liitation of this approach is that only two classes are generated and it cannot be used for ulti-channel iages. hresholding approach is sensitive to noise and intensity hoogeneities. Based on application we can select any one or cobination of ethods to get the desired segented output References []. R. C. Gonzalez and R. E. Woods, Digital Iage rocessing (rentice Hall, 00). []. Dr.Vipul Singh,,Digital Iage rocessing with MALAB and Lab VIEW; ELSEVIER 03. [3]. S. Jayaraan, S. Esakkirajan,. Veerakuar, Digital Iage rocessing, ata McGraw Hill Education rivate Liited, 009 [4]. Guang Yang, Kexiong Chen, Maiyu Zhou, Zhonglin Xu, Yongtian Chen, Study on Statistics Iterative hresholding Segentation Based on Aviation Iage, snpd. Vol., pp87-88, 8 th ACIS International Conference on software Engineering, Artificial Intelligence, Networking, and arallel/ Distributed Coputing, 007 [5]. A.S. Abutaleb, Autoatic hresholding of Gray- Level ictures using wo Diensional Entropy, Coputer Vision, Grapics, and Iage processing, vol.47, pp. -3, 989. [6]. Sale Saleh Al-ari, N.V. Kalyankar and Khaitkar S.D, Iage Segentation by using hershod echniques, Journal of Coputing,vol, pp 83-86,00. papers. Dr. Y.V.S. Lakshi is working as Manager- IR in C-DO, Bangalore. Her area of interest includes Iage processing, Nano technology, syste reliability, telecounication etc. She has published ore than 0 papers in international journals and also presented ore than 0 conference Dr.G.Wiselin Jiji is working as the rofessor and Head of the Departent of Coputer Science & Engineering in Dr.Sivanthi Aditanar College of Engineering. Her area of interest is Medical Iaging, Cognitive Science & Data Mining. She has published research articles in 40 apers in Referred international journals. She has received Career Award for Young eachers fro AICE, New Delhi. In addition, she has received 6 ore awards in National & state awards. 0