Segmentation of two dimensional electrophoresis gel image using the wavelet transform and the watershed transform

Sengar, Ratnesh Singh; Upadhyay, Ashutosh; Singh, Manjit; Gadre, Vikram M.

doi:10.1109/ncc.2012.6176861

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…And second, we adapted an unsupervised image segmentation method based on non-separable wavelets similar to those recently developed for gel electrophoresis image analysis. 86,87 The details of the image segmentation method and implementation code can be found in work of Sengar et al 87–89 The image processing workflow is as follows: (1) the original images are normalized between 0 and 1. (2) The normalized images are decomposed using an undecimated non-separable quincunx wavelet to obtain the same size decompositions for better comparison.…”

Section: Image Segmentationmentioning

confidence: 99%

Non-contact ultrasound oocyte denudation

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Section: Image Segmentationmentioning

confidence: 99%

Non-contact ultrasound oocyte denudation

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“…A comparison of WT and other filtering techniques is presented in [4]. We use WT with a Daubechies family and 5 levels of decomposition [2,4].…”

Section: Nonlinear Filteringmentioning

confidence: 99%

“…Once the proteins in the sample have been separated, the gel is then scanned and the imaged processed using computational tools. Often these 2-DGE images exhibit anomalies due to the technique itself or to the image scan and acquisition [2]. The purpose of 2-DGE image analysis is to detect the proteins (black spots) within the gel.…”

Section: Introductionmentioning

confidence: 99%

Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

et al. 2020

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Background: Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. Results: An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%. Conclusions: The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.

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“…Particularmente para imágenes 2DGE, los métodos de segmentación que se encuentran con más frecuencia en la literatura son: la detección de bordes [9], [28]- [30], métodos morfológicos [31]- [37], umbralización [31], [38]- [40], y métodos basados en regiones [41]- [43]. A continuación, se presenta una breve revisión de cada una de estas corrientes en la segmentación de imágenes 2DGE.…”

Section: Detección Y Segmentación De Proteínasunclassified

Procesamiento de imágenes de electroforesis bidimensional: una revisión

et al. 2019

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Este artículo presenta una revisión de las técnicas y algoritmos aplicados al procesamiento y análisisde imágenes de electroforesis bidimensional (2DGE). La electroforesis bidimensional permite separar cientos de proteínas en un único gel, mostrando un patrón característico. En el análisis de imágenes es importante una correcta detección de las proteínas presentes, ya que cualquier error en esta etapa puede llevar a la detección de falsas proteínas, o a obviar proteínas importantes, pero de baja abundancia, lo cual afectaría los resultados del análisis. Técnicas de segmentación son empleadas para separar las proteínas del fondo y encontrar anomalías. Los métodos empleados para la segmentación de imágenes 2DGE se pueden clasificar como: basados en detección de bordes, morfológicos, umbralización y basados en regiones. En muchos estudios proteómicos se hace necesario la fusión o registro de imágenes para la identificación y comparación de patrones de varias muestras diferentes. Para este proceso de fusión se pueden usar las imágenes originales o los resultados de la segmentación. A pesar de los avances significativos en el campo de procesamiento de imágenes 2DGE, no se encuentran en la literatura métodos completamente automatizados. Las herramientas comerciales disponibles para el análisis y procesamiento de imágenes 2DGE requieren que el usuario seleccione adecuadamente ciertos parámetros, de los cuales dependen los resultados arrojados por el software. Este artículo revisa diferentes técnicas para el procesamiento de las imágenes 2DGE. Especial atención es dada a las técnicas de segmentación y registro de imágenes.

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Segmentation of two dimensional electrophoresis gel image using the wavelet transform and the watershed transform

Cited by 9 publications

References 17 publications

Non-contact ultrasound oocyte denudation

Non-contact ultrasound oocyte denudation

Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

Procesamiento de imágenes de electroforesis bidimensional: una revisión

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