Eleni Zacharia - Academia.edu (original) (raw)

Papers by Eleni Zacharia

Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish... more Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

Spot-Segmentation, an essential stage of processing 2D gel electrophoresis images, remains a chal... more Spot-Segmentation, an essential stage of processing 2D gel electrophoresis images, remains a challenging process. The available software programs and techniques fail to separate overlapping protein spots correctly and cannot detect low intensity spots without human intervention. This paper presents an original approach to spot segmentation in 2D gel electrophoresis images. The proposed approach is based on 2D-histograms of the aforementioned images. The conducted experiments in a set of 16-bit 2D gel electrophoresis images demonstrate that the proposed method is very effective and it outperforms existing techniques even when it is applied to images containing several overlapping spots as well as to images containing spots of various intensities, sizes and shapes.

Two-Dimensional Polyacrylamide Gel Electrophoresis (2D PAGE) is a proteomic technique that allows... more Two-Dimensional Polyacrylamide Gel Electrophoresis (2D PAGE) is a proteomic technique that allows the analysis of large collections and complex mixtures of proteins. The 2D-PAGE gel images depict protein signals as spots of various intensities and sizes. In this paper, we present a novel approach to unsupervised protein spot detection in 2D-PAGE images based on a genetic algorithm. This approach involves three main steps: a) wavelet-based noise reduction, b) segmentation of the input images into regions around the local maxima of the image intensities, c) detection and model-based quantification of the spots within each region using a genetic algorithm. This algorithm searches within a multidimensional parameter space to determine, in parallel, the parameters of multiple diffusion models that optimally fit the characteristics of possible spots. The detection and quantification of the spots is achieved by superposition of diffusion functions modeling adjacent spots. Experiments with 16-bit 2D-PAGE images show that the proposed method is effective and results in low spurious spot detection rate.

Microarray gene expression image analysis is a labor-intensive task and requires human interventi... more Microarray gene expression image analysis is a labor-intensive task and requires human intervention since microarray images are contaminated with noise and artifacts while spots are often poorly contrasted and ill-defined. The analysis is divided into two main stages: Gridding and Spot-Segmentation. In this paper, an original, unsupervised and fullyautomated approach to gridding and spot-segmenting microarray images, which is based on two genetic algorithms, is presented. The first genetic algorithm determines the optimal grid while the second one determines, in parallel, the boundaries of multiple spots. Experiments on 16-bit microarray images show that the proposed method is effective and achieves more accurate gridding and spot-segmentation results in comparison with existing methods.

IEEE Transactions on Medical Imaging, 2008

Gridding microarray images remains, at present, a major bottleneck. It requires human interventio... more Gridding microarray images remains, at present, a major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully-automatic approach for accurately locating a distorted grid structure in a microarray image is presented. The gridding process is expressed as an optimization problem which is solved by using a Genetic Algorithm. The Genetic Algorithm determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94% and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.

Gridding is the first, essential stage of processing cDNA microarray images. The existing tools f... more Gridding is the first, essential stage of processing cDNA microarray images. The existing tools for allocating the grid structure in a microarray image often require human intervention which causes variations to the gene expression results. In this paper, an original and fully-automatic approach to gridding microarray images is presented. The proposed approach is based on a Genetic Algorithm which determines parallel and equidistant line-segments constituting the grid structure. Thereafter, a refinement procedure follows which further improves the existing grid structure, by slightly modifying the line-segments. Experiments on 16-bit microarray images have shown that the proposed method is effective as well as noise-resistant. Additionally, it achieves an accuracy of more than 95% and it outperforms existing methods.

Biological conclusions reached during microarray experiments can be greatly affected by human int... more Biological conclusions reached during microarray experiments can be greatly affected by human intervention, which is currently required in microarray image analysis. Therefore, accurate and automatic analysis of cDNA microarray images becomes crucial. In this paper, an automatic approach to microarray image analysis is presented. The proposed approach is based on the concept of evolution in order to process the microarray images. Conducted experiments in a set of real microarray images confirm the effectiveness of the proposed approach.

Analyzing 2D-gel electrophoresis images remains a challenging task. Amongst the respective stages... more Analyzing 2D-gel electrophoresis images remains a challenging task. Amongst the respective stages of the analysis, detection and segmentation of each individual spot are the most crucial. The commercial availability of several software programs and techniques notwithstanding, spot detection and segmentation are extremely resource intensive and frustratingly time-consuming; without human intervention, they fail to detect several protein spots, such as low intensity spots or overlapping protein, while they detect various spurious spots. This paper presents an original approach to detecting and segmenting spots in 2D-gel electrophoresis images. The conducted experiments in a set of 16-bit images demonstrate that the proposed approach is very effective and it outperforms existing techniques even when it is applied to images containing several overlapping spots as well as to images containing spots of various intensities, sizes and shapes.

Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish... more Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

Spot-Segmentation, an essential stage of processing 2D gel electrophoresis images, remains a chal... more Spot-Segmentation, an essential stage of processing 2D gel electrophoresis images, remains a challenging process. The available software programs and techniques fail to separate overlapping protein spots correctly and cannot detect low intensity spots without human intervention. This paper presents an original approach to spot segmentation in 2D gel electrophoresis images. The proposed approach is based on 2D-histograms of the aforementioned images. The conducted experiments in a set of 16-bit 2D gel electrophoresis images demonstrate that the proposed method is very effective and it outperforms existing techniques even when it is applied to images containing several overlapping spots as well as to images containing spots of various intensities, sizes and shapes.

Two-Dimensional Polyacrylamide Gel Electrophoresis (2D PAGE) is a proteomic technique that allows... more Two-Dimensional Polyacrylamide Gel Electrophoresis (2D PAGE) is a proteomic technique that allows the analysis of large collections and complex mixtures of proteins. The 2D-PAGE gel images depict protein signals as spots of various intensities and sizes. In this paper, we present a novel approach to unsupervised protein spot detection in 2D-PAGE images based on a genetic algorithm. This approach involves three main steps: a) wavelet-based noise reduction, b) segmentation of the input images into regions around the local maxima of the image intensities, c) detection and model-based quantification of the spots within each region using a genetic algorithm. This algorithm searches within a multidimensional parameter space to determine, in parallel, the parameters of multiple diffusion models that optimally fit the characteristics of possible spots. The detection and quantification of the spots is achieved by superposition of diffusion functions modeling adjacent spots. Experiments with 16-bit 2D-PAGE images show that the proposed method is effective and results in low spurious spot detection rate.

Microarray gene expression image analysis is a labor-intensive task and requires human interventi... more Microarray gene expression image analysis is a labor-intensive task and requires human intervention since microarray images are contaminated with noise and artifacts while spots are often poorly contrasted and ill-defined. The analysis is divided into two main stages: Gridding and Spot-Segmentation. In this paper, an original, unsupervised and fullyautomated approach to gridding and spot-segmenting microarray images, which is based on two genetic algorithms, is presented. The first genetic algorithm determines the optimal grid while the second one determines, in parallel, the boundaries of multiple spots. Experiments on 16-bit microarray images show that the proposed method is effective and achieves more accurate gridding and spot-segmentation results in comparison with existing methods.

IEEE Transactions on Medical Imaging, 2008

Gridding microarray images remains, at present, a major bottleneck. It requires human interventio... more Gridding microarray images remains, at present, a major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully-automatic approach for accurately locating a distorted grid structure in a microarray image is presented. The gridding process is expressed as an optimization problem which is solved by using a Genetic Algorithm. The Genetic Algorithm determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94% and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.

Gridding is the first, essential stage of processing cDNA microarray images. The existing tools f... more Gridding is the first, essential stage of processing cDNA microarray images. The existing tools for allocating the grid structure in a microarray image often require human intervention which causes variations to the gene expression results. In this paper, an original and fully-automatic approach to gridding microarray images is presented. The proposed approach is based on a Genetic Algorithm which determines parallel and equidistant line-segments constituting the grid structure. Thereafter, a refinement procedure follows which further improves the existing grid structure, by slightly modifying the line-segments. Experiments on 16-bit microarray images have shown that the proposed method is effective as well as noise-resistant. Additionally, it achieves an accuracy of more than 95% and it outperforms existing methods.

Biological conclusions reached during microarray experiments can be greatly affected by human int... more Biological conclusions reached during microarray experiments can be greatly affected by human intervention, which is currently required in microarray image analysis. Therefore, accurate and automatic analysis of cDNA microarray images becomes crucial. In this paper, an automatic approach to microarray image analysis is presented. The proposed approach is based on the concept of evolution in order to process the microarray images. Conducted experiments in a set of real microarray images confirm the effectiveness of the proposed approach.

Analyzing 2D-gel electrophoresis images remains a challenging task. Amongst the respective stages... more Analyzing 2D-gel electrophoresis images remains a challenging task. Amongst the respective stages of the analysis, detection and segmentation of each individual spot are the most crucial. The commercial availability of several software programs and techniques notwithstanding, spot detection and segmentation are extremely resource intensive and frustratingly time-consuming; without human intervention, they fail to detect several protein spots, such as low intensity spots or overlapping protein, while they detect various spurious spots. This paper presents an original approach to detecting and segmenting spots in 2D-gel electrophoresis images. The conducted experiments in a set of 16-bit images demonstrate that the proposed approach is very effective and it outperforms existing techniques even when it is applied to images containing several overlapping spots as well as to images containing spots of various intensities, sizes and shapes.