IJRIT International Journal of Research in Information Technology, Volume 2, Issue 8, August 2014, Pg. 48-51

International Journal of Research in Information Technology (IJRIT)

www.ijrit.com

ISSN 2001-5569

A Review: Image Segmentation by using Normalized Cuts, Clustering, Level Set and Mean Shift Algorithms Sakshi Jindal1, Meenakshi Bansal2 1

Department of Computer Engineering, Punjabi University Yadavindra College of Engineering, Talwandi Sabo, Punjab, India. [email protected] 2

Assistant Professor, Department of Computer Engineering, Punjabi University Yadavindra College of Engineering, Talwandi Sabo, Punjab, India. [email protected]

Abstract In this paper, review of segmentation on synthetic pictures and natural pictures are covered to reading the performance and effect of dissimilar image complexity towards segmentation procedure. That study gave some investigation findings for effective image segmentation using graph separating method with computation cost reduced. Because of its cost exclusive and it becomes disapproving in performing image segmentation on high resolution image especially in online image retrieval systems, another method had been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities, their method was more robust to initialization, faster and supplementary accurate than the well-known piecewise smooth model, third preprocesses an image by using the MS (Mean Shift) algorithm to form segmented areas that preserve the necessary discontinuity characteristics of the image. The segmented regions are then characterized by using the graph structures, and the Ncut method is functional to perform globally optimized clustering.

1. Introduction Image segmentation can be usually regarded aspartitioning an image into multiple sections. The segmentation method provides a more basic image representation as these segments can be separatelyexaminedwithout the need of human to do manual segmentation at first hand [1]. There are vast diversity of segmentation methods such as simple traditional forward segmentation with just determining the foreground and background of the image. This simplesegmentation is not sufficient in meeting demands from the current tendency of image demonstration especially in object recognition use. A more reliable segmentation is desired to counter more complex cases by applying some useful domains. Colour information is one of the popular domains used for image segmentation. To date, there are few approaches of image segmentation methods can be categorized: global information based segmentation, edge-based segmentation and section basedsegmentation [2]. The global information based segmentation identifies the threshold from a greyscale or colour strengthhistogram representing an image. This threshold acts as splitting border to segment the image into foreground and background areas. This thresholding technique is primitive such that obtaining the optimal threshold may not be easy when the image is low in contrast and holdsvarious thresholds. Edge based segmentation is more likely suitable for line seeking application such as text recognition. Discontinuity topographies are sought by using some of the popular edge detectors: Canny edge detector, Prewitt and Sobel operators [3]. Edge detector alone may not be good enough to do the segmentation since it does not guarantee forming closed boundaries which is essentialfor recognizingdifferentsegments [4]. It normally helps as base of a segmentation technique. One of the region Sakshi Jindal, IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 8, August 2014, Pg. 48-51

based method, graph partitioning, is popular for complex image subdivision especially natural image with tiny objects. Shi and Malik had formulated an improved graph subdividing algorithm called normalised cuts[5]. Graph partitioning does segmentation by inspecting an imageas a graph. Due to its algorithm complexity and finding the minimum cut for the segmentation is a NP-complete problem, it is inappropriate to be applied in real time arrangement[1]. A novel region-based technique for image segmentation. From a generally accepted model of images with intensity inhomogeneities, a local intensity clustering property, and therefore define a local clustering criterion function for the concentrations in a neighborhood of each point. Local clustering criterion is integrated over the neighborhood center to define an energy functional, which was transformed to a level set formulation. Minimization of this energy is achieved by an interleaved procedure of level set evolution and approximationof the bias field. As an important application, our method can be used for segmentation and bias correction of magnetic resonance (MR) images. The Ncut method [6], on the other hand, can be measured as aclassification technique. In most image segmentation applications, the Ncut technique is applied straight to the image pixels, which are typically of very large size and thus want huge computational complexity. For example, to use the Ncut method in [7], a gray image has to be decimated into a size of 160 × 160 pixels or smaller. In summary, it is tough to get real-time segmentation using the Ncut method. In the proposed technique, the Ncut technique was applied to the segmented districts instead of the raw image pixels. As such, it removes the major problem of the Ncut method that necessitates prohibitively high complication. By applying the Ncut method to the preprocessed regions rather than the raw image pixels, the planned method achieves a significant decrease of the computational cost and, therefore, renders real-time image division much more practically implemental. On the other hand, due to some approximation in the implementation of the Ncut method, the segmentation treating of a graph exploiting the lower dimensional region-based weight matrix also delivers more precise and robust subdividing performance compared to that based on the pixel-based weight matrix.

2. Implementation study In vertical and edges a graph G = (V, E), is created with collection ofvertices, V and edges, E. This graph theory is normally used in modelling difficulties such as traffic networks, electrical circuits and internet networks [8]. To form graph in image context, the vertices are characterized by the smallest element inthe image, namely pixels [9]. Since every pixels holdscolour information, grouping these pixels according theirsimilarities and dissimilarities can lead to attaining the imagesegmentation goal. The edges, E, are set of elementscontaining similarities and dissimilarities between pixels. In weighting segmentation, initializing a weighted graph isessential to construct the connectivity information of the pixels in an image. The edge set of E has each of them allocated with a weight . Each is a dimension of similarity between pixel and pixel . The value of rises with the similarity degree between pixel and pixel . The degree of likenesses between pixels decides important choices forgrouping them into several segments. In Cuts Graph separation is done by cutting out edges with lowvalue of weight. Weak weight of paired pixels indicates lowsimilarity between the paired pixels. As a level set method, their techniquedelivered a contour as the segmentation result. Therefore, they use the contour- based metric for precise estimation of the segmentation result. Let be a contour as a segmentation result, and be the true object boundary, which is also given as a contour. For each point, on the contour, they can calculate the distance from the point to the ground truth contour, represented by. Then, they defined the deviation from the contour to the ground truth by ………………………………………...………………………………….. (1) Which is mentioned to as the mean error of the contour. This contour-based metric can be used to estimate a sub pixel accuracy of a segmentation result given by a contour. They attentive on the application of the plannedtechnique to segmentation and bias correction of brain MR images. They first display the results for 3T MR images. Images exhibit obvious intensity inhomogeneities. The segmentation results, computed bias Sakshi Jindal, IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 8, August 2014, Pg. 48-51

fields, bias modified images. The improvement of the image quality in terms of intensity homogeneity[10] can be also established by comparing the histograms of the original images and the bias corrected images. The histograms of the original images and the bias corrected images are plotted. There are three welldefined and well-separated peaks in the histograms of the bias modified image, each corresponding to a tissue or the background in the image. Application to a 7T MR images. Original images do not have such well-separated peaks due to the mixture of the intensity distribution caused by the bias. Theirtechnique has also been tested on 7T MR images with promising results. At 7T, significant gains in image determination can be obtained due to the increase in signal-to-noise ratio. However, susceptibility-induced gradients scale with the main field, while the imaging gradients are currently limited to essentially the same strengths as used at lower field assets (i.e., 3T). Such effects are most pronounced at air/tissue interfaces, From a data-flow point of view, the outline of the planned algorithm can be categorized as the following. First, an image is segmented into severaldetachedareas using the MS algorithm. Second, the graph representation of these districts is constructed, and the variationamount between the regions is defined. Finally, a graph-partitioning algorithm based [11]on the Ncut is employed to form the final segmentation map. The regions formed by the MS segmentation can be characterized by a planar weighted region adjacency graph (RAG) G = (V, E, W) that integrates the topological information of the image structure and region connectivity. The majority of region-merging algorithms define the section dissimilarity metric as the distance among two adjacent districts in asuitable feature space. This distinction metric plays a decisive role in determining the overall performance of the image segmentation process. To define the measure of dissimilarity between neighboring districts, we first define asuitable feature space. Features like color, texture, statistical characteristics, and 2-D shape are beneficial for segmentation purposes and can be extracted from an image region and adopted the color[12] feature because it is typically the most dominant and distinguishing visual feature and adequate for a number of segmentation tasks. The average color components are computed over a region’s pixels and are labelled by a three-element color vector. When an the mean vector image is segmented based on the MS method into n regions is computed for each region, where are the mean pixel intensities of the ith region in the threedissimilar color spaces.

3. Conclusion The study leads more sympatheticon the robustness of normalised cut in terms of grouping and separating. Two-stage image segmentation can be executed to help reducingneedless image segmentation in specific region instead of performanceof segmentation on entire image An energy of the level set purposes that characterize a partition of the image area and a bias field that accounts for the intensity inhomogeneity. Segmentation and bias field approximation are therefore jointly performed by minimizing the suggested energy functional. The slowly varying property of the bias field derived from the projected energy is naturally guaranteed by the data term in variational framework, without the need to impose an explicit smoothing term on the bias field. The planned algorithm takes the advantages of the MS segmentation technique and the Ncut grouping technique, whereas their drawbacks are avoided. The use of the MS method permits the development of segments that preserve discontinuity characteristic of an image the application of the region adjacent graph and Ncut procedures to the resulting segments, rather than directly to the image pixels, yields superior image segmentation performance.

References [1] L. G. Shapiro and G. C. Stockman, 2001. [2] M. Sonka, V. Hlavac and R. Boyle, in Image Processing, Analysis, and Machine Vision, 1999. [3] T. Lindeberg, "Edge Detection”, Encyclopedia of Mathematics," Kluwer/Springer, p. 1402006098. Sakshi Jindal, IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 8, August 2014, Pg. 48-51

[4] S. C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 884-900, 1996. [5] J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 888-905, 2000. [6] K. Haris, S. N. Efstratiadis, N. Maglaveras and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging," IEEE Trans. Image Process, vol. 7, no. 12, pp. 1684-1699, 1998. [7] J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell, vol. 22, no. 8, pp. 888-905, 2000. [8] D. West, "Introduction to graph theory," 2001. [9] M. Martı́nez, P. Mittrapiyanuruk and A. C. Kak, "On combining graph-partitioning with nonparametric clustering for image segmentation," Computer Vision and Image Understanding, vol. 95, pp. 72-85, 2004. [10] N. Paragios and R. Deriche, "Geodesic active contours and level sets for detection and tracking of moving objects," IEEE Trans. Pattern Anal. Mach. Intell, vol. 22, no. 3, pp. 266-280, 2000. [11] R. Ronfard and I. J. C. Vis, "Region-based strategies for active contour models," 1994. [12] A. Tsai, A. Yezzi and A. S. Willsky, "Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification," IEEE Trans. Image Process, vol. 10, no. 8, pp. 1169-1186, 2001.

Sakshi Jindal, IJRIT

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Image Segmentation by using Normalized Cuts, Clustering, Level Set ...

IJRIT International Journal of Research in Information Technology, Volume 2, Issue 8, August 2014, Pg. 48-51. Sakshi Jindal, IJRIT. 48. International Journal of .... Such effects are most pronounced at air/tissue interfaces,. From a data-flow point of view, the outline of the planned algorithm can be categorized as the following.

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