Graph-based tools for microscopic cellular image segmentation

Ta, Vinh-Thong; Lézoray, Olivier; Elmoataz, Abderrahim; Schüpp, Sophie

doi:10.1016/j.patcog.2008.10.029

Cited by 63 publications

(34 citation statements)

References 26 publications

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“…In fluorescence labelled images of blood and bone marrow, high degrees of nuclei segmentation accuracy is reported by applying a classical image processing techniques such as shading correction and background (grayscale opening) followed by Otsu's method and watershed algorithm based on inverse distance transform [23]. In [24] a modified algorithm using the watershed algorithm based on morphological filtering operations is applied to choose the seeds of cell nuclei in tissue sections (i.e.…”

Section: Cellular and Nuclear Image Segmentation Literature Amentioning

confidence: 99%

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Hayajneh¹,

Alomari²,

Al-Shargabi³

2018

ijacsa

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Abstract-The visual extraction of cellular, nuclear and tissue components from medical images is very vital in the diagnosis routine of different health related abnormalities and diseases. The objective of this work is to modify and efficiently combine different image processing methods supported by cascaded artificial neural networks in an automated system to perform segmentation analysis of medical microscopy images to extract nuclei located in either simple or complex clusters. The proposed system is applied on a publicly available data sets of microscopy nuclei cells. A GUI is designed and presented in this work to ease the analysis and screening of these images. The proposed system shows promising performance and reduced computational time cost. It is hoped that thus system and the corresponding GUI will construct platform base for several biomedical studies in the field of cellular imaging where further complex investigations and modelling of microscopy images could take place.

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Section: Cellular and Nuclear Image Segmentation Literature Amentioning

confidence: 99%

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Hayajneh¹,

Alomari²,

Al-Shargabi³

2018

ijacsa

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“…The objective is to estimate the label of the unlabeled data from labeled ones. To address this problem, methods based upon regularization on graphs have been proposed so far (see [58] and references therein). Here, we propose to consider the clustering problem with the eikonal equation and to compute distance functions where the set corresponds to initial seeds.…”

Section: A Image Processing and Segmentationmentioning

confidence: 99%

Nonlocal PDEs-Based Morphology on Weighted Graphs for Image and Data Processing

Elmoataz

Lézoray

2011

IEEE Trans. on Image Process.

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Abstract-Mathematical morphology (MM) offers a wide range of operators to address various image processing problems. These operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In this paper, we introduce a nonlocal PDEs-based morphological framework defined on weighted graphs. We present and analyze a set of operators that leads to a family of discretized morphological PDEs on weighted graphs. Our formulation introduces nonlocal patch-based configurations for image processing and extends PDEs-based approach to the processing of arbitrary data such as nonuniform high dimensional data. Finally, we show the potentialities of our methodology in order to process, segment and classify images and arbitrary data.Index Terms-Adaptive operators, data clustering, mathematical morphology, nonlocal patch-based processing, partial differential equations (PDEs), weighted graphs.

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“…Bougleux et al [9,33,10] designed a discrete graph regularisation framework that can be seen as a digital extension of the continuous framework [38] employing a -Dirichlet regulariser. The same discrete framework has been applied in image segmentation tasks [73]. Furthermore, nonlocal differential operators have been used to derive nonlocal morphological PDEs [34].…”

Section: Nds and Graph Regularisationmentioning

confidence: 99%

Generalised Nonlocal Image Smoothing

Pizarro

Pavel²,

Didas

et al. 2010

Int J Comput Vis

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We propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness contraints that penalise general dissimilarity measures defined on image patches. One of such dissimilarity measures is the weighted 2 distance between patches. In such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain. It can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels, but also the similarity of their corresponding neighbours. This leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure. The suggested approach includes two recently proposed filters as special cases: The NLmeans filter of Buades et al. and the NDS filter of Mrázek et al. In fact, the approach introduced here can be considered as a generalisation of the latter filter. We evaluate our method for the task of denoising greyscale and colour images degraded by Gaussian and impulse noise, demonstrating that it compares very well to other more sophisticated patch-based approaches.

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Graph-based tools for microscopic cellular image segmentation

Cited by 63 publications

References 26 publications

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Nonlocal PDEs-Based Morphology on Weighted Graphs for Image and Data Processing

Generalised Nonlocal Image Smoothing

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