Article

Self-Crossing Detection and Location for Parametric Active Contours

Department of Electrical and Computer Engineering, BostonUniversity, Boston, MA 02115, USA.
IEEE Transactions on Image Processing (Impact Factor: 3.63). 02/2012; 21(7):3150-6. DOI: 10.1109/TIP.2012.2188808
Source: PubMed

ABSTRACT

Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour's angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research.

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Available from: Arie Nakhmani
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    • "In (Nakhmani & Tannenbaum, 2012), a constant number of nodes is set, and the removed nodes are replaced but in the most sparse regions. When an active contour is used to approximate complex shapes, for instance the one presented in Figure 1, it is difficult to assume a constant number of nodes. "
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    ABSTRACT: This publication presents an edge–based active contour model using the inflation/deflation force, allowing active contour nodes to be moved to find object boundaries in a digital image. The methods proposed in this study make it possible to keep a high value of the inflation/deflation force for each node until the node approaches the boundary of the analysed shape. After the boundary searched for is reached, the value of the inflation/deflation force for these nodes is automatically damped. The solutions used in this paper are of major practical significance if the analysed images contain weak boundaries and/or strong noise at the same time, and on top of that there are strictures of the shape which should be approximated. Experiments were carried out for artificial images as well as USG and MRI medical images, and have confirmed the suitability of the solutions used.
    Full-text · Article · Feb 2016 · Expert Systems with Applications
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    • "A further weakness is that the tracking precision is limited at the contour level. In recent years, the state of the art tracking algorithms based on deformable template are proposed by the papers [37] [38]. Copyright ⓒ 2015 SERSC "

    Preview · Article · Sep 2015
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    • "In publication [7], self–crossings are removed based on calculated angle values between neighbouring nodes, calculated slopes of consecutive segments and with the use of four–connected line interpolation. In publication [7], a certain constant number of nodes of the active contour approximating the moving object in a video image sequence is assumed, and the removed nodes are replaced in the most sparse regions . When segmenting shapes in medical images, which are frequently very complex, it is difficult to assume a constant number of nodes, so there is unfortunately no single solution for all possible applications . "
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    ABSTRACT: Segmenting the gallbladder from an ultrasonography (US) image allows background elements which are immaterial in the diagnostic process to be eliminated. In this project, several active contour models were used to extract the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps, anatomical changes, such as folds or turns of the gallbladder. First, the histogram normalization transformation was executed allowing the contrast of US images to be improved. The approximate edge of the gallbladder was found by applying one of the active contour models like the motion equation, a center-point model or a balloon model. An operation of adding up areas delimited by the determined contours was also executed to more exactly approximate the shape of the gallbladder in US images. Then, the fragment of the image located outside the gallbladder contour was eliminated from the image. The tests conducted have shown that for the 220 US images of the gallbladder, the area error rate (AER) amounted to 16.4%.
    Full-text · Conference Paper · Oct 2010
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