Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals

Bergeest, Jan-Philip; Rohr, Karl

doi:10.1016/j.media.2012.05.012

Cited by 61 publications

(42 citation statements)

References 32 publications

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“…This represents a significant reduction in computational cost compared to many other methods that include [30] which takes 49.2 minutes using a supervised learning approach, while in [41] it takes 30 minutes using the template matching approach. 284 | P a g e www.ijacsa.thesai.org Good computational time cost of 15 seconds were achieved by [31], but using a (Linux system of 48GB RAM). It is still thought that the proposed algorithm dramatically outperforms this time because a (Windows system of 2G RAM) is used compared to 48GB RAM.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

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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: Performance Evaluation and Discussionmentioning

confidence: 99%

“…This importance comes to enhance the understanding of cells functions [31] and enters the processing workflow of pathological diagnosis [32]. Examples of this include the immune-histochemically staining estimation and morphological grading where the detection of cell nuclei on histological slides is required.…”

Section: Clinical Nuclei Related Work Bmentioning

confidence: 99%

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Hayajneh¹,

Alomari²,

Al-Shargabi³

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…The approach proposed in (Bergeest and Rohr 2012) is based on active contours and level sets for cell nuclei segmentation in fluorescence microscopy images. Authors have developed two approaches.…”

Section: Segmentationmentioning

confidence: 99%

“…(Zhang, Wang, and Shi 2009), (Bergeest and Rohr 2012), (Tarnawski et al 2013), (Liao et al 2015), (Mouelhi et al 2013) Clustering These techniques are used in the first exploratory data analysis and to group patterns that are similar. Sometimes they are combined with other techniques.…”

Section: Segmentationmentioning

confidence: 99%

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Gamarra

Zurek

San-Juan

2017

Journal of Information Systems Engineering & Management

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Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

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“…Deformable models, in particular, have been succesfully employed, for example, in the segmentation of organic cells [12], motion tracking [13] and fluid flow [14]. The key idea is to define a dynamic curve that evolves over the image domain towards the desired regions or object boundaries.…”

Section: Introductionmentioning

confidence: 99%

A Highly Accurate Level Set Approach for Segmenting Green Microalgae Images

Borges

Hamann

Silva

et al. 2015

2015 28th SIBGRAPI Conference on Graphics, Patterns and Images

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Abstract-We present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae's boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image. By characterizing the distributions of image intensity values in local regions, we are able to automatically classify image regions into background and algae regions. We present results obtained with our method. These results are very promising as they document that we can achieve highly accurate algae segmentations when comparing ours against manually segmented images (segmented by an expert biologist) and with results derived by other approaches covered in the literature.

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Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals

Cited by 61 publications

References 32 publications

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Cascades Neural Network based Segmentation of Fluorescence Microscopy Cell Nuclei

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

A Highly Accurate Level Set Approach for Segmenting Green Microalgae Images

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