Guo & Zhu, 2007). The classical definition for image segmentation is as follows:

Image Segmentation, Available Techniques, Developments and Open Issues Tranos Zuva, Oludayo O. Olugbara, Sunday O. Ojo and Seleman M. Ngwira Guo & Zhu, 2007). The classical definition for image segmentation is as follows: Let the image domain be Ω and be partitions of Ω Such that, =, =, = Abstract - In areas such as computer vision and image processing, image segmentation has been and still is a relevant research area due to its wide spread usage and application. Its accuracy but very elusive is very crucial in areas as medical, remote sensing and image retrieval where it may contribute to save, sustain and protect human life. This paper provides a survey of achievements, problems being encountered, and the open issues in the research area of image segmentation and usage of the techniques in different areas. In this survey we also suggested what must be done in order for researchers to test their techniques performance and to compare them among other segmentation techniques. We considered the techniques under the following three groups: Threshold-based, Edge-based and Region-based. Key Words Threshold-based, Edge-based, image segmentation, Region-based Introduction The main goal of image segmentation is domain independent partitioning of an image into a set of disjoint regions that are visually different, homogeneous and meaningful with respect to some characteristics or computed property(ies), such as grey level, texture or colour to enable easy image analysis (object identification, classification and processing) (Freixenet, Munoz, Raba, Marti & Cufi, 2002), (Lucchese & Mitra, 2001), (Wang, where = for, and each is connected. (1) Discontinuity and similarity/homogeneity are two basic properties of the pixels in relation to their local neighbourhood used in many segmentation methods. The segmentation methods that are based on discountinuity property of pixels are considered as boundary or edges based techniques and that are based on similarity or homogeneity are region based techniques. We have intentionally separated thresholding technique from region based due the usage of histogram and its simplicity in application (Freixenet et al., 2002). Hybrid based techniques are derived from integration of the edge and region based techniques information (Wang et al., 2007). It must noted that so many image segmentation surveys have been conducted but there very few who have presented how researchers can evaluate one s technique against the other on a domain independent images or evaluate the performance of their segmentation (Zhang, 2001), (Min, Powell & Bowyer, 2004), (Udupa, LeBlanc, Zhuge, Imielinska, Schmidt, Currie, Hirsch & Woodburn, 2006). Many surveys have been directed to one area of application of image 20

segmentation in areas such as medical, remote sensing and image retrieval (Freixenet et al., 2002),(Lucchese & Mitra, 2001),(Deb, 2008). This paper will be organized as follows: Thresholding Methods Boundary/Edge Based methods Region based methods Region based Vs Edge based methods Performance Evaluation Summary. Fig.1 indicates the classification of image segmentation techniques we have considered in this paper. The methods explained and used to segment the image in fig.2 and fig.3 were used only to clarify the segmentation methods. Image segmentation would have been easy if not because of; Image noise Weak object boundaries Inhomogeneous object region Weak contrast and Many others that affect images. Threshold- Based Shape Segmentation Techniques Edge-Based Region- Based Thresholding Method Thresholding based image segmentation aims to partition an input image into pixels of two or more values through comparison of pixel values with the predefined threshold value T individually; Let, be an image, 0,, <, = 1,, Where, refers to the pixel value at the position,. Thresholding may be implemented locally or globally. In global thresholding the image is partitioned into two as shown above while local thresholding the image is subdivided into subimages and the threshold for each subimage is derived from the local properties of the pixels. The predefined value of T is the one that complicates this method. The determination of the value T has been the point of interest in image segmentation research (Cheriet, Said & Suen, 1998), (Dawoud & Kamel, 2004), (Hu, Hoffman & Reinhardt, 2001). There have been many algorithms developed to generate better threshold value T to segment an image (Dawoud & Kamel, 2004). These methods that use intensity value do not use spatial morphological image information of an image and they usually fail to segment objects with low contrast or noisy images with varying background (Rekik, Zribi, Hamida & Benjelloun, 2009). Gradient Laplacien Region- Growing Classifiers/ Clustering Failure to find the most suitable algorithm to determine the threshold value(s) T the result might be one or all of the following: 21 General- Purpose Knowledge -Based

1. The segmented region might be smaller or larger than the actual 2. The edges of the segmented region might not be connected 3. Over or under-segmentation of the image (arising of pseudo edges or missing edges) Edge Based Methods Edge based segmentation is the location of pixels in the image that correspond to the boundaries of the objects seen in the image. It is then assumed that since it is a boundary of a region or an object then it is closed and that the number of objects of interest is equal to the number of boundaries in an image. For precision of the segmentation, the perimeter of the boundaries detected must be approximately equal to that of the object in the input image. In the endeavour to implement the above there was need to define an edge in an image. An edge or a linear feature is manifested as an abrupt change or a discontinuity in digital number of pixels along a certain direction in an image. The manifestation becomes a highgradient/extreme of first order derivative or a zero crossing in the second derivatives. This brought another assumption that every object of interest in an image has a boundary that can be detected through the use of gradient or second derivative. Sobel, Prewitt and watershed just to mention a few use templates based on gradient/first derivative to detect the boundaries of an image. The Laplacian template is based on second derivative. These methods in general can be defined by function g(x) that will act as stopping term when the object/region boundary has been reached. A function g(x) can be defined as 0 lim =0 For instance, =,,, 1 (3) Where, is the convolution of the image with the Gaussian filter, which results in a smoother version of image, where,, = /,, = >0, =0, (4) There are problems that have been areas of interest for researchers and the problems are centred on the use of gradient to detect the boundaries [(Chan & Vese, 2001)]. For instance, these methods have problems with images that are: Edge-less Very noisy Boundary that are very smooth Texture boundary Other problems of these techniques emanate from the failure to adjust/calibrate gradient function accordingly thus produces undesirable results as: The segmented region might be smaller or larger than the actual 22

The edges of the segmented region might not be connected Over or under-segmentation of the image (arising of pseudo edges or missing edges) Input Logo-Image a Fig.2 Edge Based Method (Sobel) Fig.2 illustrates some the problems that are encountered in the use of edged based methods. The edges of fig.2 a. can be seen missing in fig.2 b. and this causes problems in post-segmentation image processing, e.g. in retrieval or registration. Region Based Methods Segmented Logo The region based segmentation is partitioning of an image into similar/homogenous areas of connected pixels through the application of homogeneity/similarity criteria among candidate sets of pixels. Each of the pixels in a region is similar with respect to some characteristics or computed property such as colour, intensity and/or texture. The assumption in these techniques is that the partitions that are formed correspond to objects or meaningful parts of the image. In (Wang et al., 2007) the most commonly used techniques are the following: b Thresholding Region Growing Classifiers Clustering The region growing is a mostly used classical segmentation technique. These region growing based segmentation models share the following assumption about the image pixel properties The intensity values within each region/object conforms to Gaussian Distribution The mean intensity value for each region/object is different (Global Mean) (Wang, He, Mishra & Li, 2009). The Gaussian probability distribution function (pdf) for the region is given as follows:, = Where =mean, 2 = variance. (5) With this type of segmentation, the problems of discontinuous edges and no segmentation of objects without edges have been eliminated. The boundary of an object can be identified using the edge/boundary pixels of a region ensuring that the boundary is closed and the segmentation of objects without edges can now be done. One of the region based technique was introduced by Chan & Vese Active Contour without Edges can detect contours 23

with or without edges. These methods are capable of detecting and preserving boundaries without the need to smooth the input image, even when it is very noisy. Images with smooth boundaries no longer cause any problems (Chan & Vese, 2001). Lots of interest have been shown to perfect these methods and encouraging results have been produced. For instance Jundong Liu argued that the global mean used by Chan & Vese in their model was not the best for medical images. The argument centred on the Chan & Vese model that defines the evolving curve C in Ω and an energy function,,. Chan & Vese model minimizes the energy function defined as follows:,, =. h +. + + (6) where are averages of inside C and outside C respectively. The values of from the above energy function are global values computed from the entire image. In his paper Robust Image Segmentation using Local Median he alluded that the drawback that existed in most region based active contours were overcame. The paper indicates that the drawbacks originated from the assumption that the intensity values globally conforms to Gaussian distribution within each region and that global mean is enough to be used as discriminant measure. In order to improve the region based segmentation Liu minimized the following energy function:,, =. h +. + + (7) In this function global mean were replaced by local medians respectively. Where = = W is a rectangle window that is used to define neighbourhood pixels in an image. The functions are defined to calculate the two local medians for the neighbouring pixels that are inside and outside the moving curve respectively on the image domain. Liu emphasised on the use of local information in an image instead of the global information. (Wang L, He L, Mishra A, LI C, 2009) in their paper Active contour driven by local Gaussian distribution fitting energy tend to agree with Liu in that local information of an image is very important in segmentation. They indicated that the use of global information as in Active contours without edges segmentation fail to adequately segment images with intensity inhomogeneity. Most the images that cause the problems to segmentation techniques that use global information of an image are from medical field such as microscopy, computer tomography (CT), Ultrasound, 24

magnetic resonance imaging (MRI), Positron Emission Tomography (PET), and mammography. Wang etc used Gaussian distribution to describe the local image intensities with different means and variances. They concluded that their method was able to deal with both noise and intensity in-homogeneity but has high computational time. The computational cost of these methods has been one of limiting factors in their usage (Ayed & Mitiche, 2008). These methods have to start with an initial curve and its placement on the image plays an important role in the final product of the segmentation process. Chan & Vese indicated that in their method Active Contour without Edges, the initial curve can be placed anywhere in the image and the segmentation of an image is competitively good. This shows that researchers are kin to make these methods domain independent. Failure to adjust the homogeneity/similarity criteria accordingly will produce undesirable results. The following are some of them: 1. The segmented region might be smaller or larger than the actual 2. Over or under-segmentation of the image (arising of pseudo objects or missing objects) 3. Fragmentation[(Varshney, Rajpal & Purwar, 2009)] Input Logo-Image Segmented Logo a. Original Image b. Segmented Fig.3 Region Based Method (Chan & Vese) Fig.3 indicates some of the problems that can be encountered using the region based methods. It can be observed that there are some addition and subtraction to region of interest. Again this will affect the postsegmentation image processing. Region Based Methods Vs Edge Base Segmented Logo a. Region Based Method Segmented Logo b. Edge Based Method Fig.4 Segmented Logos 25

Region Based Methods Mark the whole region of the object(each pixel of image is marked whether it belongs to object or not e.g.fig.4a, white (p(i,j)=1) indicates object region) Homogeneity/si milarity is used to determine the regions of the object When the method misses or adds some parts of the object, it is possible to reconstruct the object without the original image with high probability of success as can be observed on fig 4a. Some methods can segment edge-less images High computation intensive making the methods less Edge Based Methods Mark the edges of the object(each pixel of image is marked whether it belongs to the boundary of the object or not e.g. fig4b, white (p(i,j)=1) indicates edges) First derivative or second derivative is used to determine the edge When the method misses part of the boundary or adds an edge of an object, it is possible to reconstruct the boundary but with low probability of success as can be observed on fig4b. Cannot segment edge-less images Low computation intensive making the methods more ideal for use ideal for use Table 1 Region based Vs Edge based methods Performance Evaluation There have been many image segmentation methods created and being created using many distinct approaches and algorithms but still it is very difficult to assess and compare the performance of these segmentation techniques (Zhang, Fritts & Goldman, 2008). Researchers would evaluate their image segmentation techniques by using one or more of the following evaluation methods in Fig.5. Human Assisted/Evaluated Subjective Segmentation Evaluation Techniques System-Level Objective Analytical Machine Evaluated Direct Goodness Empirical Fig.5 An Overview of Evaluation Techniques Discrepancy 26

The full description of the above evaluation methods can be found from (Zhang et al., 2008), (Polak, Zhang & Pi, 2009). Most of these methods ideally should be domain independent but in reality they are domain dependent. It is believed that it is difficult to develop a single model that applies to all image objects (Boucheron, Harvey & Manjunath, 2007). Both the subjective and objective evaluation have been used to evaluate segmentation techniques but within a domain dependent environment (Zhang et al., 2008). It can be appreciated that whatever method used in a specific domain has been used to compare the segmentation technique in that domain. These methods have been used to adjust parameters of the segmentation techniques in order to solve the following problems in segmentation area: The segmented region might be smaller or larger than the actual The edges of the segmented region might not be connected Over or under-segmentation of the image (arising of pseudo edges or missing edges) It is very sad that (Hu et al., 2001) concluded that there is no segmentation method that is better than the other in all domains. We believe that with the use of universal evaluation methods we can be able to find the segmentation techniques that we may say are better than others in all domains. Challenges and Future Directions For us to find domain independent segmentation techniques is when we can evaluate the techniques by domain independent evaluation methods using a domain independent image database. In order for this to happen we need to create the universal image database such that researchers can use this database to evaluate their techniques. Whether a subjective or objective evaluation method is used the image database must be same and the images must be ranked to enable comparison of segmentation techniques. When researchers segment these images in the database they must indicate the value of parameters for each image segmented, the computational time and specification of the machine used. This will enable easy selection of segmentation technique for a particular area. Due to ad hoc form of research, this way of evaluating techniques will give some form of order in segmentation field. There is still room of improvement in each group of segmentation methods, that is; Edge-based Region-based. Summary We have looked at the segmentation techniques, performance evaluation methods and we can give the following summary; Segmentatio n Methods Thresholding Research interest Determine the value of T Known Problems in segmenting images Low contrast Spatial morphologic al 27

Edge Based (threshold value) Determine the appropriate Stopping gradient or other stopping criteria Region Based Determin e homogen eity/simil arity criteria to decompos e the image into regions. Determin e how to deal with inhomogen eity in images All three of them: Threshold ing Edge Based Region Based Determin e performa nce evaluatio n of the technique s Determin e informati on Edge-less Noisy images Smooth boundaries Texture boundaries High computation al time The segment ed region might be smaller or larger than the 28 Table 2. Summary References comparis on criteria of the technique s actual The edges of the segment ed region might not be connect ed Over or undersegment ation of the image (arising of pseudo edges or missing edges) AYED, I. B. & MITICHE, A. 2008. A Region Merging Prior for Variational Level Set Image Segmentation. IEEE, 17(12):2301-2311. BOUCHERON, L. E., HARVEY, N. R. & MANJUNATH, B. S. 2007. A quantitative object-level metric for segmentation performance and its application to cell nuclei. springer-verlag 2007:208-219. CHAN, T. F. & VESE, L. A. 2001. Active Contours Without Edges. IEEE, 10(2):266-277.

CHERIET, M., SAID, J. N. & SUEN, C. Y. 1998. A Recursive Thresholding Technique for Image Segmentation. IEEE, 7(6). DAWOUD, A. & KAMEL, M. S. 2004. Iterative Multimodel Subimage Binarization for Handwritten Character Segmentation. IEEE, 13(9):1223-1230. DEB, S. 2008. Overview of image segmetation techniques and searching for future directions of research in content-based image retrieval. FREIXENET, J., MUNOZ, X., RABA, D., MARTI, J. & CUFI, X. 2002. Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. Springer:408-422. HU, S., HOFFMAN, E. A. & REINHARDT, J. M. 2001. Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images. IEEE, 20(6):490-498. LUCCHESE, L. & MITRA, S. K. 2001. Colour image segmentation: A state-of-the-art survey.207-221. MIN, J., POWELL, M. & BOWYER, K. W. 2004. Automated performance evaluation of range image segmentation algorithms. IEEE, 34(1):263-271. al. 2006. A framework for evaluating image segmentation algorithms. Computerized Medical Imaging and Graphics, 30( ):75-87. VARSHNEY, S. S., RAJPAL, N. & PURWAR, R. 2009. Comparative study of image segmentation techniques and object matching using segmentation Paper presented at the International Conference on Methods and Models in Computer Science. WANG, L., HE, L., MISHRA, A. & LI, C. 2009. Active contours driven by local Gaussian distribution fitting energy. Signal Processing. WANG, Y., GUO, Q. & ZHU, Y. 2007. Medical image segmentation based on deformable models and its applications Springer:209-260. ZHANG, H., FRITTS, J. E. & GOLDMAN, S. A. 2008. Image Segmentation Evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding, 10(2):260-280. ZHANG, Y. J. 2001. A review of recent evaluation methods for image segmentation. Paper presented at the International Symposium on Signal Processing and its Applications (ISSPA), Kuala Lumpur. POLAK, M., ZHANG, H. & PI, M. 2009. An evaluation metric for image segmentation of multiple objects. Image and Vision Computing, 27(8):1223-1227. REKIK, A., ZRIBI, M., HAMIDA, A. B. & BENJELLOUN, M. 2009. An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k- Means Clustering Algorithm Initialization. Internationaal Journal of Signal Processing, 5(1). UDUPA, J. K., LEBLANC, V. R., ZHUGE, Y., IMIELINSKA, C., SCHMIDT, H., CURRIE, L. M., et 29