A geometric snake model for segmentation of medical imagery

Yezzi, Anthony; Kichenassamy, Satyanad; Kumar, Arun; Olver, Peter J.; Tannenbaum, Allen

doi:10.1109/42.563665

Cited by 487 publications

(312 citation statements)

References 46 publications

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“…Each of these approaches has its own benefits and drawbacks. Geodesic active contours [6,29] are based on a continuous formulation (computing geodesics in Riemannian spaces), and produce minimal geometric artifacts. Standard variational techniques for computing geodesic contours (e.g.…”

Section: Introductionmentioning

confidence: 99%

Computing geodesics and minimal surfaces via graph cuts

Boykov

Kolmogorov

2003

Proceedings Ninth IEEE International Conference on Computer Vision

460

524

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Section: Introductionmentioning

confidence: 99%

Computing geodesics and minimal surfaces via graph cuts

Boykov

Kolmogorov

2003

Proceedings Ninth IEEE International Conference on Computer Vision

460

524

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“…1 (a) after applying the topographic region growth algorithm. Second, the scheme applies an active contour algorithm [24] to improve mass segmentation. The active contour is a deformable curve controlled by an internal and an external force.…”

Section: A Automated Detection and Classification Of Mass Region Spimentioning

confidence: 99%

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

et al. 2007

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Rationale and Objectives: The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions. Materials and Methods:A large and diverse reference library involving 3000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion. Results:In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (p = 0.128). Conclusion:This study suggests that using the new automated ICAD scheme, the inter-observer variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (p < 0.05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

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“…The initial contour generally has to be close to the desired contour to avoid being trapped in a local minimum of the energy function that does not correspond to the true object boundary. Interactive algorithms generally need image-specific initialization (Yezzi et al 1997;Corsi et al 2002;Horsch et al 2002;Wolf et al 2002;Fenster and Downey 2003;Lin et al 2003) and/or allow interactive correction of results (Koning et al 2002;Wolf et al 2002). Some procedures start by placing initial points of the contour close to the desired feature, using operators (Corsi et al 2002;Fenster and Downey 2003;Lin et al 2003) or an initial seed (Yezzi et al 1997;Horsch et al 2002;Wolf et al 2002).…”

Section: Automated Segmentationmentioning

confidence: 99%

Segmentation of elastographic images using a coarse-to-fine active contour model

Liu¹,

Zagzebski²,

Varghese³

et al. 2006

Ultrasound in Medicine & Biology

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Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.

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A geometric snake model for segmentation of medical imagery

Cited by 487 publications

References 46 publications

Computing geodesics and minimal surfaces via graph cuts

Computing geodesics and minimal surfaces via graph cuts

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

Segmentation of elastographic images using a coarse-to-fine active contour model

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