Graph cut based segmentation of convoluted objects

2008

Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the "shrinking bias". To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard.

Advances in Intelligent Systems and Computing, 2014

We propose an algorithm to segment 2D ellipses or 3D ellipsoids. This problem is of fundamental importance in various applications of cell biology. The algorithm consists of minimizing a contrast invariant energy defined on sets of non overlapping ellipsoids. This highly non convex problem is solved by combining a stochastic approach based on marked point processes and a graph-cut algorithm that selects the best admissible configuration. In order to accelerate the computing times, we delineate fast algorithms to assess whether two ellispoids intersect or not and various heuristics to improve the convergence rate. Keywords Nuclei segmentation • 2D and 3D images • Graph-cuts • Marked point processes • Ellipses and ellipsoids • Multiple objects detection • Multiple birth and cut • Bio-imaging E. Soubies (B) • X. Descombes

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011

Though graph cut based segmentation is a widely-used technique, it is known that segmentation of a thin, elongated structure is challenging due to the "shrinking problem". On the other hand, many segmentation targets in medical image analysis have such thin structures. Therefore, the conventional graph cut method is not suitable to be applied to them. In this study, we developed a graph cut segmentation method with novel Riemannian metrics. The Riemannian metrics are determined from the given "initial contour," so that any level-set surface of the distance transformation of the contour has the same surface area in the Riemannian space. This will ensure that any shape similar to the initial contour will not be affected by the shrinking problem. The method was evaluated with clinical CT datasets and showed a fair result in segmenting vertebral bones.

2012

ABSTRACT We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the lp norm, 1≤ p≤∞. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC).

2008 Ieee 16th Signal Processing Communication and Applications Conference, 2008

A graph theoretic color image segmentation algorithm is proposed, in which the popular normalized cuts image segmentation method is improved with modifications on its graph structure. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions, instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities of the neighboring regions. The irregular distribution of the nodes, as a result of such a modification, causes a bias towards combining regions with high number of links. This bias is removed by limiting the number of links for each node. Finally, segmentation is achieved by bipartitioning the graph recursively according to the minimization of the normalized cut measure. The simulation results indicate that the proposed segmentation scheme performs quite faster than the traditional normalized cut methods, as well as yielding better segmentation results due to its regionbased representation.

Lecture Notes in Computer Science, 2009

Graph cuts have become very popular in many areas of computer vision including segmentation, energy minimization, and 3D reconstruction. Their ability to find optimal results efficiently and the convenience of usage are some of the factors of this popularity. However, there are a few issues with graph cuts, such as inherent sequential nature of popular algorithms and the memory bloat in large scale problems. In this paper, we introduce a novel method for the approximation of the graph cut optimization by posing the problem as a gradient descent formulation. The advantages of our method is the ability to work efficiently on large problems and the possibility of convenient implementation on parallel architectures such as inexpensive Graphics Processing Units (GPUs). We have implemented the proposed method on the Nvidia 8800GTS GPU. The classical segmentation experiments on static images and video data showed the effectiveness of our method.

Lecture Notes in Computer Science, 2011

Graph cut algorithms are very popular in image segmentation approaches. However, the detailed parts of the foreground are not segmented well in graph cut minimization.There are basically two reasons of inadequate segmentations: (i) Data-smoothness relationship of graph energy. (ii) Shrinking bias which is the bias towards shorter paths. This paper improves the foreground segmentation by integrating the statistical significance measure into the graph energy minimization. Significance measure changes the relative importance of graph edge weights for each pixel. Especially at the boundary parts, the data weights take more significance than the smoothness weights. Since the energy minimization approach takes into account the significance measure, the minimization algorithm produces better segmentations at the boundary regions. Experimental results show that the statistical significance measure makes the graph cut algorithm less prone to bias towards shorter paths and better at boundary segmentation.

2010

Over the last few years energy minimization has emerged as an indispensable tool in computer vision. The primary reason for this rising popularity has been the successes of efficient graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity estimation. The scale and form of computer vision problems introduce many challenges in energy minimization.

2012

The graph partitioning has been widely used as a mean of image segmentation. One way to partition graphs is through a technique known as Normalized Cut, which analyzes the graph’s Laplacian matrix eigenvectors and uses some of them for the cut. This work proposes the use of Normalized Cut in graphs generated by structures based on Quadtree and Component Tree to perform image segmentation. Experiments of image segmentation by Normalized Cut in these models are made and a specific benchmark compares and ranks the results obtained by other graph-conversion techniques proposed in the literature. The results are promising and allow us to conclude that the use of different graph models combined with the Normalized Cut can yield better segmentations according to the characteristics of images. Keywords-Image Segmentation; Normalized Cut; Quadtree; Component Tree