Research On Image Segmentation Methods And Application

Image segmentation is a process of dividing an image into different regions such thateach region is, but the union of any two adjacent regions is not, homogeneous. Imagesegmentation is the first step in image understanding and pattern recognition. The basicdevelopment process of the image segmentation is as follows: the early classicalsegmentation methods based on image intensity and gradient, such as thresholding,contour-based, and region-based; the eighties active contour models, such as parametricactive contours and geometric active contours; the segmentation methods with priorknowledge, such as active shape model and active appearance model. From thedevelopment process, we can observe that the intelligence and ability of the imagesegmentation become better and better. In this paper, the following information is mainlyused to segment objects, such as intensity, shape, texture and motion. We first analyze aclassical image segmentation method, image binarization, and then discuss thesegmentation methods by combining shape priors and active contours and the texturesegmentation. Finally we give some object tracking methods. According to the research ofthis paper, we want to form a union image segmentation framework with various cues(such as intensity, shape, texture, motion etc), which has a high intelligent ability in orderto solve some complex image segmentation problems. This paper also shows oneapplication, namely the left ventricle magnetic resonance (MR) image segmentation. Theprimary work and remarks of this paper are as follows:(1) This paper presents a new double-threshold image binarization method based onthe edge and intensity information. We first find the seeds near the image edges, andpresent an edge connection method to close the image edges. Then, we use closed imageedges to partition the binarized image that is generalized using a high threshold, and obtainthe primary binarization result by filling the partitioned binarized image with the seeds.Finally, the final binarization result is obtained by remedying the primary binarizationresult with the binarized image generalized using a low threshold. Compared with theclassical binarization methods and the binarization methods similar with our method thatare based on the edge information, our method is effective on the binarization of imageswith low contrast and inhomogeneous object intensities.(2) We propose a level set implementation of the Mumford-Shah model integratingprior shape statistical knowledge. The statistical shape based approach to the image segmentation using levcl sets mainly consists of the constructions of the prior shape modeland the shape energy term. Aiming at these two parts, we mainly do two pieces of work: (1)A simple and feasible construction method of the prior shape model is proposed, which isbased on binary images; (2) A new construction method of shape energy term is presented,which considers the global and local shape information at the same time, and withoutintroducing pose parameters makes evolving surface stable.(3) This paper describes a fast and active texture segmentation approach based on theorientation and the local variance. First, a set of feature images are extracted using theorientation and the local variance. To reduce the computational complexity, a separabilitymeasurement method, which is used for selecting four feature images with goodseparability in four orientations, is proposed in this paper. To improve the segmentation,we adopt a nonlinear diffusion filtering to smooth the four feature images. Finally, avariational framework incorporating these features in a level set based, unsupervisedsegmentation process is adopted. To improve the computational speed, instead of solvingthe Euler-Lagrange equation, we calculate the energy, with level set representation, tosolve the variational framework. Segmentation results of various synthetic and realtextured images has demonstrated that our method has good performance and efficiency.(4) This paper presents three object tracking methods based on object contours:●This paper present an object tracking method using edge-based shape matching isproposed, which presents a new shape similarity measurement according to the imageedge. To guarantee the tracking precision and handle the local distortion of objects, wepresent a new shape similarity measurement method based on the edge image in theweighted narrow band.●A parametric active contour model is presented for object tracking based on matchingdegree image of object contour points. We first construct a matching degree imageaccording to object contour points, and track the object using parametric activecontours. This paper presents a new feature matching approach and a new directionalfilter. Assuming that the motion of objects is small in this paper, we constrain themotion of object contour within the contour vicinity defined by a band, which isconstructed by the generation method of narrow band of level set. Experimental resultsdemonstrate that our method can effectively track rigid and non-rigid objects.●This paper presents a two-stage object tracking method by combining region-basedmethod and contour-based method. First, a kernel-based method is adopted to locatethe object region. Then diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial targetposition is predicted and evaluated by Kalman filter and Bhattacharyya coefficient,respectively. In the contour evolution stage, the active contour is evolved based on theobject feature image that is generated with the color space component array (CSCA).In the process of the evolution, similarities of the target region are compared to ensurethat the object contour evolves in the right way.(5) This paper presents two segmentation methods of left ventricle MR images:●According to the characteristics of tagged MR images, we introduced an automaticsegmentation method based on multistage hybrid processing. First the left ventricle islocated using morphological method, and then the inner and outer contours areinitialized using k-mean clustering, templet matching and the myocardium shaperestoration based on skeleton. At last, the initial contour lines are evolved usingimproved level set method to achieve object boundaries.●According to the characteristics of MR images without tag lines, we present animproved shape statistics variational approach for the outer contour segmentation ofleft ventricle MR images. We use the Mumford-Shah model in an object feature spaceand incorporate the shape statistics and an edge image to the variational framework.The introduction of shape statistics can improve the segmentation with brokenboundaries. The edge image can enhance the weak boundary and thus improve thesegmentation precision. The generation of the object feature image which hashomogenous "intensities" in the left ventricle facilitates the application of theMumford-Shah model.