Boundary finding with parametrically deformable models

Staib, Lawrence H.; Duncan, James S.

doi:10.1109/34.166621

Cited by 677 publications

(418 citation statements)

References 42 publications

Supporting

Mentioning

408

Contrasting

Unclassified

Order By: Relevance

“…More complex internal energy functions, e.g. incorporating prior shape knowledge, have also been reported in literature [10,11]. The external energy is derived from the image, so that the snake will be attracted to features of interest.…”

Section: Snakesmentioning

confidence: 99%

See 1 more Smart Citation

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Vylder

Ochoa

Philips

et al. 2011

Computer Vision/Computer Graphics Collaboration Techniques

View full text Add to dashboard Cite

Abstract. Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset.

show abstract

Section: Snakesmentioning

confidence: 99%

“…If the objects of interest has a specific shape or if they have more pronounced local features, e.g. jags, then the internal probability could be formulated using a more complex shape model, such as the models proposed in [11,10].…”

Section: Internal Probabilitymentioning

confidence: 99%

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Vylder

Ochoa

Philips

et al. 2011

Computer Vision/Computer Graphics Collaboration Techniques

View full text Add to dashboard Cite

show abstract

“…On the other hand, deformable templates have the capacity to deal with shape deformations [32,33]. The major methods include active contours [34,35,36] and analytical and prototype based parametric models [37,38,39].…”

Section: Image Recognition Using Templatesmentioning

confidence: 99%

Inter-image outliers and their application to image classification

James

Dimitrijev

2010

Pattern Recognition

View full text Add to dashboard Cite

Image variability that is impossible or difficult to restore by intra-image processing, such as the variability caused by occlusions, significantly reduces the performance of image-recognition methods. To address this issue, we propose that the pixels associated with large distances obtained by inter-image pixel-by-pixels comparisons should be considered as inter-image outliers and should be removed from the similarity calculation used for the image classification. When this method is combined with the template-matching method for image recognition, it leads to state-of-the-art recognition performance: 91% with AR database that includes occluded face images, 90% with PUT database that includes pose variations of face images and 100% with EYale B database that includes images with large illumination variation.

show abstract

“…An active contour is a curve that evolves from an initial shape toward a final solution, under external-force action and internal-force reaction. This curve is usually approximated by a set of nodes that form the vertices of a polygon (12), by a parametric curve described using a polynomial (spline) function (13), or by a harmonic model (14).The success of these models is due to their great adaptability: active contours can conform to a large variety of shapes, and they include mechanisms for interactive correction of errors. However, the best results are obtained when the initialization is close to the actual location of the boundaries.…”

mentioning

confidence: 99%

Quantification of multicontrast vascular MR images with NLSnake, an active contour model: In vitro validation and in vivo evaluation

Mansard

Soulas

Anwander

et al. 2004

Magnetic Resonance in Med

View full text Add to dashboard Cite

Vessel-wall measurements from multicontrast MRI provide information on plaque structure and evolution. This requires the extraction of numerous contours. In this work a contour-extraction method is proposed that uses an active contour model (NLSnake) adapted for a wide range of MR vascular images. This new method employs length normalization for the purpose of deformation computation and offers the advantages of simplified parameter tuning, fast convergence, and minimal user interaction. The model can be initialized far from the boundaries of the region to be segmented, even by only one pixel. The accuracy and reproducibility of NLSnake endoluminal contours were assessed on vascular phantom MR angiography (MRA) and high-resolution in vitro MR images of rabbit aorta. An in vivo evaluation was performed on rabbit and clinical data for both internal and external vessel-wall contours. In phantoms with 95% stenoses, NLSnake measured 94.3% ؎ 3.8%, and the accuracy was even better for milder stenoses. In the images of rabbit aorta, variability between NLSnake and experts was less than interobserver variability, while the maximum intravariability of NLSnake was equal to 1.25%. In conclusion, the NLSnake technique successfully quantified the vessel lumen in multicontrast MR images using constant parameters. The determination of lumen narrowing by an angiographic technique is still the reference measurement for evaluating atherosclerosis. However, measurements in the vessel wall would be more predictive. High-resolution MRI has emerged as a powerful noninvasive technique for the evaluation of vessel-wall abnormalities. Its usefulness for the in vitro and in vivo study of plaque evolution has been demonstrated in humans and in animal models (1-7). The use of multicontrast MRI with measurements of the lumen and outer vessel-wall circumference, area, and thickness, and the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) in the vessel wall provides information on plaque structure and evolution (8,9). These measurements are generally based on a manual extraction of numerous contours, which requires at least two medical experts to preserve objectivity. Thus, with the increasing number of MRI exams, a fast, automated postprocessing algorithm is necessary for multicenter longitudinal studies. However, there are several difficulties related to the characteristics of the MR data. Acquisitions are performed in a multicontrast mode (such as MR angiography (MRA), high-resolution T 1 , T 2 , and proton density), with the lumen blood appearing black or white. SNR and CNR in the surrounding tissues are highly dependent on the acquisition parameters (e.g., spatial resolution and fat saturation) and the vessel-wall composition. Vessel-wall signal is often heterogeneous in pathologic cases. Thus, it is difficult to detect inner and outer contours automatically. Moreover, minimal user-interaction is required, and the process should be fast and reproducible.In this study we describe a method based on an activecontour model adapt...

show abstract

Boundary finding with parametrically deformable models

Cited by 677 publications

References 42 publications

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Leaf Segmentation and Tracking Using Probabilistic Parametric Active Contours

Inter-image outliers and their application to image classification

Quantification of multicontrast vascular MR images with NLSnake, an active contour model: In vitro validation and in vivo evaluation

Contact Info

Product

Resources

About