Automatic registration for images of two-dimensional protein gels

Smilansky, Zeev

doi:10.1002/1522-2683(200105)22:9<1616::aid-elps1616>3.0.co;2-z

Cited by 107 publications

(78 citation statements)

References 28 publications

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“…The elastic transformation field is not parametrized and the regularization term is based on its quadratic energy. The methods in [51], [52] produce a piecewise-bilinear mapping. In [51], the grid is generated by a Delaunay triangulation, while a regular grid is deformed to adapt to the image characteristics in [52].…”

Section: A Controlled Data 1) Accuracymentioning

confidence: 99%

“…The methods in [51], [52] produce a piecewise-bilinear mapping. In [51], the grid is generated by a Delaunay triangulation, while a regular grid is deformed to adapt to the image characteristics in [52]. None of these two algorithms benefits from landmarks or from regularization in the optimization process, a limitation that we overcome with the algorithm proposed in this paper.…”

Section: A Controlled Data 1) Accuracymentioning

confidence: 99%

See 1 more Smart Citation

Elastic Registration of Biological Images Using Vector-Spline Regularization

Sorzano

Thévenaz

Unser

2005

IEEE Trans. Biomed. Eng.

269

192

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Abstract-We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases.

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Section: A Controlled Data 1) Accuracymentioning

confidence: 99%

Section: A Controlled Data 1) Accuracymentioning

confidence: 99%

Elastic Registration of Biological Images Using Vector-Spline Regularization

Sorzano

Thévenaz

Unser

2005

IEEE Trans. Biomed. Eng.

269

192

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“…Fully and directly exploiting the image intensities [7], the intensity-based gel image registration algorithms have the advantages of no segmentation required and most importantly, these methods have potentials to achieve automated registration. However, the computation of this category of schemes not efficient.…”

Section: Introductionmentioning

confidence: 92%

Hybrid Registration for Two-Dimensional Gel Protein Images

Wang¹,

Feng²

2005

Proceedings of the 3rd Asia-Pacific Bioinformatics Conference

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Image registration technique is fundamental and essential to the accurate and efficient analysis of protein sequence data. But due to the elastic deformations of two-dimensional gel protein eletrophoresis images, their registration still remains a challenge. In this paper, a hybrid 2D gel protein image registration method is proposed. In the first stage of registration, the wavelet-based hierarchical registration which fully exploits the image intensity is used to correct global displacements and affine transformation parameters are achieved. In the second stage, the landmark-based elastic registration is introduced to correct local displacements and enhance registration performance and accuracy. In the proposed method, the hierarchical registration from low resolution to high resolution can accelerate the registration convergence and hence good computational efficiency can be achieved. Though wavelets have been widely used in image processing, its application in the area of gel protein image registration is not well investigated. In this paper, the wavelet-based hierarchical approach is introduced in 2D gel protein image registration. The algorithm makes use of the merits of the existing categories of registration techniques and achieves high performance registration results automatically.

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“…To the best of our knowledge, the only registration methods that work directly on the electrophoretic image information can be found in [5,6,7,8]. The method in [5] solves a differential equation for the transformation field after smartly deriving an image formation model.…”

Section: Introductionmentioning

confidence: 99%

Elastic Image Registration with Applications to Proteomics

Sorzano¹,

Thévenaz²,

Valdés³

et al. 2006

AIP Conference Proceedings

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Abstract. In proteomics, electrophoretic 2-D gels are used to separate the proteome according to the molecular weight and electrical charge of its constituents, which are proteins expressed by a cell or organ at a particular time and under specific conditions. One of the main applications is the analysis of differential expressions between different conditions for which certain (perhaps many) spots are present in one of the images, but not in the other. One of the difficulties of this analysis is that 2-D gels are affected by spatial distortions due to run-time differences and dye-front deformations, which results in images that are significantly dissimilar and that pose a challenging problem to image-registration algorithms. In this paper, we test the efficiency of a stateof-the-art elastic-registration algorithm that we had already introduced in the context of biomedical images [1]. We study here the registration of simulated 2-D gels with known expression patterns and deformations. We show that our algorithm is capable of handling such situations. The proposed algorithm is publicly available at http://bigwww.epfl.ch under the name UnWarpJ.

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Automatic registration for images of two-dimensional protein gels

Cited by 107 publications

References 28 publications

Elastic Registration of Biological Images Using Vector-Spline Regularization

Elastic Registration of Biological Images Using Vector-Spline Regularization

Hybrid Registration for Two-Dimensional Gel Protein Images

Elastic Image Registration with Applications to Proteomics

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