Detection of Spatially Correlated Objects in 3D Images Using Appearance Models and Coupled Active Contours

Mosaliganti, Kishore; Gelas, Arnaud; Gouaillard, Alexandre; Noche, Ramil R.; Obholzer, Nikolaus; Megason, Sean G.

doi:10.1007/978-3-642-04271-3_78

Cited by 17 publications

(16 citation statements)

References 8 publications

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“…This is similar to multiple level set approaches (e.g. [12,4,9,14,8]). However, in our case we do not need a coupling term to prevent level sets from merging because we perform the minimization within previously segmented regions.…”

Section: Globally Optimal Cell Segmentationmentioning

confidence: 61%

“…To solve (8) we use the Split Bregman method. This method is a general technique for efficiently solving L1-regularized problems and for iteratively finding extrema of convex functionals [6].…”

Section: Split Bregman Methodsmentioning

confidence: 99%

“…One can distinguish between parametric models and implicit models (e.g. [12,4,9,14,8,10,5]). Implicit representations based on level sets have gained increased interest since topological changes can be handled naturally.…”

Section: Introductionmentioning

confidence: 99%

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Fast Globally Optimal Segmentation of Cells in Fluorescence Microscopy Images

Bergeest

Rohr

2011

Lecture Notes in Computer Science

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Abstract. Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.

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Section: Globally Optimal Cell Segmentationmentioning

confidence: 61%

Section: Split Bregman Methodsmentioning

confidence: 99%

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Fast Globally Optimal Segmentation of Cells in Fluorescence Microscopy Images

Bergeest

Rohr

2011

Lecture Notes in Computer Science

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“…Nonetheless, these models are subject to the same labeling constraints so as to avoid assigning different labels to overlapping regions. A practical solution to overcome this limitation is to evolve each curve independently [6] and let the shape prior control the boundary in the overlapping region.…”

Section: Introductionmentioning

confidence: 99%

Level Set with Embedded Conditional Random Fields and Shape Priors for Segmentation of Overlapping Objects

Shah

2011

Computer Vision – ACCV 2010

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Abstract. Traditional methods for segmenting touching or overlapping objects may lead to the loss of accurate shape information which is a key descriptor in many image analysis applications. While experimental results have shown the effectiveness of using statistical shape priors to overcome such difficulties in a level set based variational framework, problems in estimation of parameters that balance evolution forces from image information and shape priors remain unsolved. In this paper, we extend the work of embedded Conditional Random Fields (CRF) by incorporating shape priors so that accurate estimation of those parameters can be obtained by the supervised training of the discrete CRF. In addition, non-parametric kernel density estimation with adaptive window size is applied as a statistical measure that locally approximates the variation of intensities to address intensity inhomogeneities. The model is tested for the problem of segmenting overlapping nuclei in cytological images.

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“…Implicit models using level sets have gained increased interest for cell segmentation since topological changes can be handled naturally (e.g., Ortiz de Solorzano et al, 2001;Dufour et al, 2005;Nath et al, 2006;Chang et al, 2007;Palaniappan et al, 2007;Yan et al, 2008;Maška et al, 2009;Mosaliganti et al, 2009;Padfield et al, 2009;Ersoy et al, 2009;Dzyubachyk et al, 2010;Xu et al, 2011). Ortiz de Solorzano et al (2001) developed a two-step level set approach for segmenting cells, which employs a gradient-based energy functional.…”

Section: Introductionmentioning

confidence: 99%