&lt;title&gt;Segmentation of medical images using an active-contour model incorporating region-based image features&lt;/title&gt;

Poon, Colin S.; Braun, Michael; Fahrig, Rebecca; Ginige, Athula; Dorrell, Andrew

doi:10.1117/12.185238

Cited by 19 publications

(7 citation statements)

References 14 publications

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“…Also, the distance calculated at the optimal position in the first frame is stored and considered to be optimal. The internal force that acts on the vertex is then defined as a force proportional to the relative change in length (10) where is the constant elastic coefficient. The form of (10) indicates that in the th row of the stiffness matrix, all elements Introduction of such a force prevents the opposite sides of the contour from collapsing.…”

Section: B Contour Stiffness and Internal Forcesmentioning

confidence: 99%

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Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

Mikić

Krucinski

Thomas

1998

IEEE Trans. Med. Imaging

220

119

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This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

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Section: B Contour Stiffness and Internal Forcesmentioning

confidence: 99%

“…Various approaches have been proposed for finding the optimal contour. Among them are finite-difference methods [3], [4], [11], finite-element methods [5], [6], [11], dynamic programming [7], [8], and simulated annealing [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

Mikić

Krucinski

Thomas

1998

IEEE Trans. Med. Imaging

220

119

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“…The use of global energy minimization approaches, such as dynamic programming and simulated annealing, have been proposed [1,27,13] as an alternative to the more common local optimization schemes. The speed of convergence of these techniques is an issue as is the ability to describe the correct segmentation result using energy functionals.…”

Section: Active Contour Models (Snakes)mentioning

confidence: 99%

User-defined B-Spline template snake

Dehmeshki¹

2021

Preprint

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This thesis has created a new Snake model that overcomes many of the limitations of the traditional finite difference snake. This new deformable model combines a novel user initialization process with a finite element B-spline snake to create a powerful semi-automatic segmentation method. Using the simple but powerful initialization process, the user recognizes critical points and regions in a specified order, and transfers this knowledge to the model. By drawing lines across the object of interest, importatn information pertaining to the global shape of the object, such as width and symmetry, is imparted to the model. The snake is parameterized using minimum number of model degrees of freedom necessary and these degrees of freedom are placed in optimal positions around the object, based on the critical points and features recognized by the user via the input lines. Thus, the model is more like a deformable template than a local snake model - it is less sensitive to noise and more amenable to propagation to subsequent image slices in a volume image or time series. Unlike a traditional deformable template model however, it is constructed and positioned by the user rather than preconstructed and automatically initialized by the segmentation system. The template snake isinitialized very close to the object boundary and is very similar in shape. Furthermore, it is "aware" of its position with respect to the object. This thesis also describes the computation of the external image forces and how the known initial position and shape of the snake can be used to design object-specific image forces.

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“…Forces that drive the snake through spurious features, such as edges, have been proposed by Cohen [7]. Various image features that can decrease the sensitivity of the snake to its initial placement in an image have been explored [9][10][11] and forces that capture a wide range of shapes and produce boundaries of concave regions have been proposed too [12,13].…”

Section: Introductionmentioning

confidence: 99%