Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation
Issue No. 11 - November (2002 vol. 24)
<p> <div><b>Disclaimer</b></div> <div>A claim of priority in research and publication appeared on page 1486 in the paper "Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation" by J.K. Udupa, P.K. Saha, and R.A. Lotufo (<em>IEEE Transactions on Pattern Analysis and Machine Intelligence</em>, vol. 24, no. 11, pp. 1485-1500, Nov. 2002) with respect to the paper "Multiseeded Segmentation Using Fuzzy Connectedness" by G.T. Herman and B.M. Carvalho (<em>IEEE Transactions on Pattern Analysis and Machine Intelligence</em>, vol. 23, no. 5, pp. 460-474, May 2001). Furthermore, the wording of this claim suggests professional misconduct on the part of G.T. Herman and B.M. Carvalho. </div> <div>Responsibility for the content of published papers rests with the authors. The peer review process is intended to determine the overall significance of the technical contribution of a manuscript. The peer review process does not provide a way to validate every statement in a manuscript. In particular, the IEEE has not validated the claim referred to in the first paragraph above. The IEEE regrets publishing this unauthenticated statement and the pain that such publication may have caused to G.T. Herman and B.M. Carvalho.</div></p> <p><b>Abstract</b>—The notion of fuzzy connectedness captures the idea of "hanging-togetherness" of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1,000s of images in a variety of applications. In the previous framework, a fuzzy connected object is defined with a threshold on the strength of connectedness. In this paper, we introduce the notion of relative connectedness that overcomes the need for a threshold and that leads to more effective segmentations. The central idea is that an object gets defined in an image because of the presence of other co-objects. Each object is initialized by a seed element. An image element <em>c</em> is considered to belong to that object with respect to whose reference image element <em>c</em> has the highest strength of connectedness. In this fashion, objects compete among each other utilizing fuzzy connectedness to grab membership of image elements. We present a theoretical and algorithmic framework for defining objects via relative connectedness and demonstrate utilizing the theory that the objects defined are independent of reference elements chosen as long as they are not in the fuzzy boundary between objects. An iterative strategy is also introduced wherein the strongest relative connected core parts are first defined and iteratively relaxed to conservatively capture the more fuzzy parts subsequently. Examples from medical imaging are presented to illustrate visually the effectiveness of relative fuzzy connectedness. A quantitative mathematical phantom study involving 160 images is conducted to demonstrate objectively the effectiveness of relative fuzzy connectedness.</p>
Fuzzy connectedness, image segmentation, object definition, digital topology.
J. K. Udupa, P. K. Saha and R. A. Lotufo, "Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 1485-1500, 2002.