Image processing of microscopic cellular samples

Aguirre, Paulina

doi:10.1109/melcon.2012.6196450

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Transmission Electron Microscopy (TEM) images, several digital filters have been introduced by Kushwaha et al 7 such as median or Wiener filter. A similar work proposed by Sim et al 8 and Aguirre 9 based on employing an adaptive Wiener filter to enhance the effectiveness of the classical Wiener filter by considering the noise variance. Luisier et al 10 have suggested a Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) technique for denoising images corrupted with Poisson noise.…”

Section: Introductionmentioning

confidence: 99%

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Nasser

Boudier

2018

Preprint

View full text Add to dashboard Cite

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Several nuclei segmentation approaches have been reported in the literature. However, most of them require fine-tuning of many parameters. Furthermore, they can only be applied to particular models or acquisition systems. As a result, there is a crucial need for a generic method to segment noisy and densely packed nuclei in microscopy images. In this paper, we present a sparse representation method for denoising of microscopy images. The proposed method works properly in the context of most challenging cases. In addition, it provides a denoised image and simultaneously the potential locations of nuclei. To evaluate the performance of the proposed method, we tested our method on two datasets from cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96.96% recall for C.elegans dataset. Besides, in Drosophila dataset, our method achieved very high recall (99.3%).

show abstract

Section: Introductionmentioning

confidence: 99%

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Nasser

Boudier

2018

Preprint

View full text Add to dashboard Cite

show abstract

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Nasser

Boudier

2019

Sci Rep

View full text Add to dashboard Cite

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).

show abstract