Technical Review on Cluster Integrated Active Contour for Image Segmentation

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 08 | February 2016 ISSN (online): 2349-784X

A Technical Review – Cluster Integrated Active Contour for Image Segmentation Suthar Viral Girishbhai PG student Department of Electronics & Communication L D College of Engineering, Ahmadabad

Prof. Preeti. J. Muliya Associate professor Department of Electronics & Communication L D College of Engineering, Ahmadabad

Abstract Given an image, we enable to detect the shape of object in it. This problem like many image processing problem, has application in industry, especially in area of pattern recognition and motion capturing. In this study for the purpose of feature extraction in given image. Active contour is a set of points which enclose target feature the future to extracted .This detect the object whose boundaries are not define by gradient. Model automatically detect interior contour, starting with only initial curve. In this using different active contour model we extract the feature using energy minimization. In this paper, we have presented a review of various active contour models for image segmentation. Through this method which will helpful in medical image segmentation and accurately extracts the object.

Keywords: active contour, image segmentation, level set method, c-v model ________________________________________________________________________________________________________ I.

INTRODUCTION

Image segmentation main aim to divide an image to some parts, which correlate strongly by object of reality. Image segmentation is a difficult task because of big variability of object shapes, and different image quality .the purpose of segmentation is to decompose the image in two parts that are meaningful with respect to a particular application.” The neurophysiologists ‘and psychologists’ believe that figure and ground constituted one of the fundamental problems in vision was reflected in the attempts of workers in computer vision to implement a process called segmentation. The partitioning problem is to delineate region that have, to a certain degree, coherent attributes in the image .we will refer to this problem as partition problem it is important problem because, on the whole, object and coherent physical process in the scene project in to region with coherent image attributes. By incorporating prior information into segmentation approach, the accuracy and robustness of segmentation procedure can be improved greatly.Thus the image partition problem can be viewed as a first approximation to the scene partitioning problem.

II. REVIEW OF PAPERS T.chan and L.vase”Active contour without edges” IEEE trans.on image processing, (2001) pp.266-277: The authors represent active contour which detect object in given image use the technique of curve evolution like Mumford-shah functional for segmentation and level sets. This model use energy minimization approach. Active contours or a snake is to model the movement of a dynamic curve towards an object’s boundary under the influence of internal and external forces. Internal forces control the smoothness of the curve, while external forces lead the curve to the boundary until convergence is achieved. In this model consider c as a curve and consider two energy inside c and outside c and final fitting energy which will be represented by;

The fitting energy is minimized when curve on the boundary of the object. In this model author will minimize fitting energy and use regularization terms.

The Mumford-shah functional for segmentation is [13]:

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A Technical Review – Cluster Integrated Active Contour for Image Segmentation (IJSTE/ Volume 2 / Issue 08 / 041)

Where Îą,β are positive parameters, the solution image u obtain by minimizing this functional is formed by smooth regions R i with sharp boundaries, denoted by C.Our active contour model is minimal partition problem, where best approximation u of u0,as a function taking only two values, average (u0)inside C u= { average(u0)outsideC, Level set function for the evolving curve C is represented by the zero level set of the lipschitz continuous function Ă˜:ℌ→ đ??źđ?‘….so ,C={(đ?‘Ľ, đ?‘Ś) ∈ â„Ś: Ă˜(đ?‘Ľ, đ?‘Ś) = 0},and choose Ă˜ positive inside C and negative outside C. Here we replace unknown variable C by unknown variable Ă˜ new energy function represent by F(Ă˜,c 1,c2),becomes:

The model can detect object whose boundary not defined by gradient or smooth boundary. Model automatically detect interior contours; start with only one initial curve. The model will apply to various images. jixia zhang, Haibo wang, shaoguo liu, Frannck Davoine, chunhong pan, shiming xiang.� Active learning based automatic face segmentation for kinect video�. IEEE transaction on speech and signal processing,(2013)pp.1816-1820: Authors present new segmentation approach for extracting faces from video. It uses active learning frame work. In this paper author use automatic method in which segmentation done automatically without human need. In old model the information provide by human [6, 4].In this first applies active learning to local spline regression based segmentation (LSR-seg). LSR-Seg classifies all the pixels into face and non-face given the labels of partial points. Active learning is then utilized to improve its performance. This is conducted by sampling query points based on the segmentation results and labeling them by an oracle.

These labeled points are employed for the next LSR-Seg. That process iterates until no segmentation improvement is achieved.

The query points are labeled automatically using both color and depth information.

The author apply the model for the face segmentation and apply to the video captured with kinect which provide both RGB and depth information. In this author make a comparison of proposed Method with GrabCut [4],LSR-seg [6]and GrabCutD [14],and result shows that proposed method performs approximately as LSR-seg but containing less outliners. Accurate face regions were obtained under different poses, scales and background.

Alaa khadidos,victor Sanchez and chang-Tsun Li.�Active contour based on weighted gradient vector flow and balloon force for medical image segmentation�, IEEE Transaction on Image processing (2014) pp. 902-906: In this paper author propose a new external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. For removing high noise sensitivity and poor performance over weak edges which are the most acute issues that reduce the segmentation accuracy of these curves, particularly in medical images. These balloon forces improve convergence when the snake is initialized far from the desired boundary. However, if the strength of the balloon forces is too high, the snake may not detect weak edges resulting in snake leakages. Gradient vector flow (GVF) field as an external force The GVF force increases the capture range of the snake, allowing it to conform to concave boundaries. This external force, however, may still fail to accurately converge to the desired boundary if the levels of noise in the image are high [9].

GVF force requires the computation of the gradient vector of the edge map, ∇f. In this work, the edge map f(x, y) is computed by first calculating the Eigen values and eigenvectors of the 2 Ă— 2 Hessian matrix HM x,y,L, for each pixel at position (x, y).

Fexternal = (FBalloon ∗ (1 − Ί)) + (FGVF ∗ Ί) GVF forces tend to be the main acting external forces, helping the snake conform to the object’s boundary. In this paper author apply the external force on real slices of MRI and CT sequences to evaluate the performance of the proposed external force compared to snakes using three different external forces: a) GVF force exclusively [2], b) balloon and image gradient (B Grad) forces [10], and c) balloon or GVF forces based on thresholding (BGVFT). In this method the external force is either balloon or GVF. Balloon forces guide the snake to the object’s boundary, while in the presence of strong edge information GVF forces make the snake conform to the boundary. Our proposed approach is compared to snakes using GVF forces, balloon forces and a combination of GVF and balloon forces based on manual thresholding. Experimental results on real medical images show that the proposed external force outperforms the other evaluated external forces, and minimizes snake leakages, while offering the advantage of initializing the snake with a single manually selected point inside the desired region.

Lujun Zhang, Mingyan Jiang, Mingging yang.�An improve local distribution fitting model.�

School of information science and engineering, Shandong University, jinan, 250100, china, IEEE 2010.

In this paper author present improved local distribution fitting model base on recently proposed local binary fitting model and local distribution fitting model this new model incorporate particle swarm optimization(PSO)in to Gaussian mixture model(GMM)based intensity distribution estimation to optimize the parameter estimation. T. Chan and L. Vese [1] proposed the piecewise constant (PC) model, which is a well-known region-based model, assuming the various image regions with constant intensity. Thus this model is not reasonable for images with intensity in homogeneity. Later some advanced piecewise smooth

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A Technical Review – Cluster Integrated Active Contour for Image Segmentation (IJSTE/ Volume 2 / Issue 08 / 041)

(PS) models [2-4] have been proposed to improve the performance of PC models. However, these models using global statistics have been found to be not ideal for heterogeneous images and also computationally expensive More recently, localized regionbased models have been proposed to overcome the defects of PC and PS models. Chunming Li et al [5] proposed a regionscalable fitting (RSF) energy function, which is also known as an integration of local binary fitting (LBF) energy over the neighborhood of each image point. Other similar localized region-based models can be seen in [6, 7]. Such models all take the neighborhood pixels information of each image pixel into account, thus present the advantage over PC and PS models. However, most LBF models are rather sensitive to initial contour location.

This paper uses PSO algorithm to optimize the parameter estimation. In this paper real images with multiple objects in homogeneous background or complex object shapes. The image with bacteria has the feature that the objects are always locally darker than background, which is not true for the image with cups. Thus we can see from the results, for the image with cups, we can get better segmentation result using our model than using original LDF model. To the image with bacteria for which the original LDF model is suitable, using our model we can get as good result as using the original LDF model. Experimental results on synthetic and real images show the improved performance of our LDF model over the original model. Thus our model can be applied widely and achieve more accurate result III. SUMMARY OF LITERATURE SURVEY In this various methods based on ACM have been studied. From this study the active contour extracts the object from image automatically. From the above literature survey, we have finalized the flow of algorithm which can be generalised for any application involving image processing.

Active contour is an energy minimization process it is clear that C–V model can detect objects whose boundaries are not represented by gradient or with very smooth boundaries, for which classical active contour models are not applicable. The position of the initial curve can be anywhere in the image, and it does not necessarily surround the object to be detected. But the model fails to segment the images with intensity inhomogeity. It uses automatic method in which active learning frame work& segmentation done automatically without human requirement. In old model the information provide by human. New external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. At weak edge poor performance and also higher noise so required two force for that.

REFERENCE jixia zhang, Haibo wang, shaoguo liu, Frannck Davoine, chunhong pan, shiming xiang.” Active learning based automatic face segmentation for kinect video”. IEEE transaction on speech and signal processing, (2013) pp.1816-1820.

[2] Alaa khadidos, victor Sanchez and chang-Tsun Li.”Active contour based on weighted gradient vector flow and balloon force for medical image segmentation”,IEEE Transaction on Image processing(2014)902-906.

[3] Chuanzhen Rong, Yongxing jia, Yu yang, Ying zhu, Yuan Wang.”Interactive image segmentation based on Geodesic Active Regions”. IEEE on signal processing (2013) pp.1-5.

[4] Oren freifeld, Hayit Greenspan, Jacob Goldberger.”Lesions Detection In Noisy Mr. Brain Images Using Constrained GMM and Active Contour”. IEEE Transaction on biomedical imaging (2007) pp.596-599.

[5] Lujun Zhang, Mingyan Jiang, Mingging yang.”An improve local distribution fittingmodel”,IEEE Transaction on image and signal processing (2010) pp.1322-1326.

[6] Gai pan, Liqun Gao , Zhaohua Cui .” Active contour driven by local entropy energy function for segmentation and bias correction”, IEEE Transaction on control and decision conference (2013) pp.1492-1495.

[7] Na Zhang,Jianxun Zhang and Ruizhi Shi.”An improved Chan-vase Model for Medical Image segmentation”,IEEE on computer science and software engineering (2008) pp.864-867.

[8] Chunming Li, Chiu-yen Kao, John C.Gore, and Zhaohua Ding.”Minimization of Region-Scalable Fitting Energy for Image Segmentation”,IEEE Transaction on image processing (2008) pp.1940-1949.

[9] Tony Chan and Luminita Vese,” An Active Contour Model without Edges”, IEEE Transaction on image processing (2001) pp.266-277.

[10] H.Li and K.N.Ngan,”Automatic video segmentation and tracking for content-basedmultimedia services,”IEEE Commun.Mag., vol.45, pp.27-33, January 2007.

[11] https://en.wikipedia.org/wiki/Active_contour_model/last assessed on December 2015.

[12] https://en.wikipedia.org/wiki/Image_segmentation/last assessed on December 2015.

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