User-Steered Image Segmentation Paradigms: Live Wire and Live Lane

Falcão, Alexandre X.; Udupa, Jayaram K.; Samarasekera, Supun; Sharma, Shoba; Hirsch, Bruce Elliot; Lotufo, Roberto de Alencar

doi:10.1006/gmip.1998.0475

Cited by 417 publications

(359 citation statements)

References 30 publications

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“…For every edge, a cost is assigned to represent the "likelihood" that the edge belongs to a desired boundary in the image. How this cost function should be chosen is discussed in, e.g., [1]. When the user places a seed-point, Dijkstra's algorithm [12] is used to compute the optimal path to the seed-point from all other points in the image.…”

Section: The 2d Live-wire Methodsmentioning

confidence: 99%

“…Often, we also need some high-level knowledge about the type of objects we are interested in. This is recognized in [1], where the segmentation process is divided into two steps: recognition and delineation. Recognition is the task of roughly determining where in the image the objects are located, while delineation consists of determining the exact extent of the object.…”

Section: Introductionmentioning

confidence: 99%

“…Live-wire [1,4] is a popular semi-automatic segmentation method that has been used successfully for many 2D problems. Various ways of extending this method to segment volume images have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

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A 3D Live-Wire Segmentation Method for Volume Images Using Haptic Interaction

Malmberg

Vidholm

Nyström

2006

Discrete Geometry for Computer Imagery

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Abstract. Designing interactive segmentation methods for digital volume images is difficult, mainly because efficient 3D interaction is much harder to achieve than interaction with 2D images. To overcome this issue, we use a system that combines stereo graphics and haptics to facilitate efficient 3D interaction. We propose a new method, based on the 2D live-wire method, for segmenting volume images. Our method consists of two parts: an interface for drawing 3D live-wire curves onto the boundary of an object in a volume image, and an algorithm for connecting two such curves to create a discrete surface.

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Section: The 2d Live-wire Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A 3D Live-Wire Segmentation Method for Volume Images Using Haptic Interaction

Malmberg

Vidholm

Nyström

2006

Discrete Geometry for Computer Imagery

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“…The strength μ(p) of a path is defined as the strength of its κ-weakest link; that is, (1) when l > 1, and μ(p) = 1 for l = 1. For c, d ∈ A ⊆ C the fuzzy κ-connectedness strength in A between c and d is defined as the strength of a strongest path in A between c and d; that is, (2) If κ is a hard binary relation, κ: C × C → {0, 1}, then the relation μ A is known as a transitive closure of κ ∩ (A × A). Note that It is also not difficult to see (and it follows easily from Proposition 2.1 below) that…”

Section: Basic Definitions and Notationmentioning

confidence: 99%

“…We now overcome this theoretical challenge and generalize its theory to multiple objects. (2) In this process of generalization, several most fundamental properties of AFC, RFC, and IRFC have been uncovered. They allow us to better understand the behavior of the FC process in general, and IRFC in particular, and give us a single unified theoretical framework within which all members of FC family methods can be described elegantly.…”

Section: Introductionmentioning

confidence: 99%

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Ciesielski

Udupa

Saha

et al. 2007

Computer Vision and Image Understanding

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In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa, Saha, Lotufo [15] and Saha, Udupa [14].) Our theory, which solves a problem of Udupa and Saha from [13], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification.The first iteration step of the IRFC algorithm gives a segmentation known as relative fuzzy connectedness, RFC, segmentation. Thus, the IRFC technique is an extension of the RFC method. Although the RFC theory, due to Saha and Udupa [19], is developed in the multi object/multi seed framework, the theoretical results presented here are considerably more delicate in nature and do not use the results from [19]. On the other hand, the theoretical results from [19] are immediate consequences of the results presented here. Moreover, the new framework not only subsumes previous fuzzy connectedness descriptions but also sheds new light on them. Thus, there are fundamental theoretical advances made in this paper.We present examples of segmentations obtained via our IRFC based algorithm in the multi object/ multi seed environment, and compare it with the results obtained with the RFC based algorithm. Our results indicate that, in many situations, IRFC outperforms RFC, but there also exist instances where the gain in performance is negligible.

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Analytical surface recognition in three-dimensional (3D) medical images using genetic matching: Application to the extraction of spheroidal articular surfaces in 3D computed tomography data sets

Jacq

Roux

Stindel

et al. 2000

Int. J. Imaging Syst. Technol.

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This paper tackles the problem of the in situ extraction of specific geometrical primitives from a three‐dimensional (3D) biomedical data set. This task involves two main problems: segmentation of major structures and extraction of the features of interest. The segmentation algorithm studied focuses on cortical bone structures. It proceeds through an analysis of a 3D watershed transform applied to the contrast image and outputs numerical surfaces as basin borders. The feature extraction task focuses on the identification of smooth regions exhibiting homogeneous curvatures, i.e., articular surfaces. We hypothesize that such surfaces can be accurately modeled through the zero set of a second‐order polynomial surface. Tracking the set of optimal parameters makes use of global and local optimization procedures, both working in the same encoding framework. The latter is a minimal subset encoding scheme. In this scheme, a model under test is indirectly described through the minimal set of data points that it interpolates, i.e., nine points in the quadratic model case. Optimization is reached by maximizing an objective function accounting for the point‐matching score of a fuzzy representation of the geometrical model vs. data points of the numerical surfaces. As such, a huge search space does not enable exhaustive exploration (e.g., the Hough transform) and the global search step makes use of a canonical genetic algorithm (i.e., a stochastic process). The latter outputs a fitness‐ordered set of solutions, which is not the best one. The subsequent local search acts as a refinement step; it performs an iterative approximation that merges some suboptimal solutions of the final‐ordered set coming from the global search. This search process is applied to the in situ extraction of spheroidal joint surfaces with the help of an ellipsoidal model. The whole algorithm is shown to be accurate and time efficient.© 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 30–43, 2000

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User-Steered Image Segmentation Paradigms: Live Wire and Live Lane

Cited by 417 publications

References 30 publications

A 3D Live-Wire Segmentation Method for Volume Images Using Haptic Interaction

A 3D Live-Wire Segmentation Method for Volume Images Using Haptic Interaction

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Analytical surface recognition in three-dimensional (3D) medical images using genetic matching: Application to the extraction of spheroidal articular surfaces in 3D computed tomography data sets

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