Eleni Zacharia

An Original Genetic Approach to the Fully Automatic Gridding of Microarray Images

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 (GA). The GA 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.

3-D Spot Modeling for Automatic Segmentation of cDNA Microarray Images

Spot segmentation-the second essential stage of cDNA microarray image analysis-constitutes a challenging process. At present, several up-to-date spot-segmentation techniques or software programs-proposed in the literature-are often characterized as “automatic.” On the contrary, they are in effect not fully automatic since they require human intervention in order to specify mandatory input parameters or to correct their results. Human intervention, however, can inevitably modify the actual results of the cDNA microarray experiment and lead to erroneous biological conclusions. Therefore, the development of an automated spot-segmentation process becomes of exceptional interest. In this paper, an original and fully automatic approach to accurately segmenting the spots in a cDNA microarray image is presented. In order for the segmentation to be accomplished, each real spot of the cDNA microarray image is represented in a three-dimensional (3-D) space by a 3-D spot model. Each 3-D spot model is determined via an optimization problem, which is solved by using a genetic algorithm. The segmentation of real spots is conducted by drawing the contours of their 3-D spot models. The proposed method has been compared with various published and established techniques, using several synthetic and real cDNA microarray images that contain thousands of spots. The outcome has shown that the proposed method outperforms prevalent existing techniques. It is also noise resistant and yields excellent results even under adverse conditions such as the appearance of spots of various sizes and shapes.

Microarray image analysis based on an evolutionary approach

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.

Grow-Cut Based Automatic cDNA Microarray Image Segmentation

Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images. Images suffer from noise, artifacts, and uneven background, while spots depicted on images can be poorly contrasted and deformed. In this paper, an original approach for the segmentation of cDNA microarray images is proposed. First, a preprocessing stage is applied in order to reduce the noise levels of the microarray image. Then, the grow-cut algorithm is applied separately to each spot location, employing an automated seed selection procedure, in order to locate the pixels belonging to spots. Application on datasets containing synthetic and real microarray images shows that the proposed algorithm performs better than other previously proposed methods. Moreover, in order to exploit the independence of the segmentation task for each separate spot location, both a multithreaded CPU and a graphics processing unit (GPU) implementation were evaluated.

An effective approach for detection and segmentation of protein spots on 2-d gel images

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ~ 21 400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

A Spot Segmentation Approach for 2D Gel Electrophoresis Images Based on 2D Histograms

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.

An Unsupervised and Fully-Automated Image Analysis Method for cDNA Microarrays

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 fully-automated 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.

Microarray image gridding via an evolutionary algorithm

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.

2D-GE spot detection combining multidirectional texture and spatial intensity cues

Spot detection is a challenging task of 2D Gel Electrophoresis image analysis. The available software packages and techniques miss some of the protein spots while they detect a high number of spurious spots. This paper introduces a novel approach for the detection of protein spots on 2D gel images which is based on multidirectional texture and spatial intensity information. The proposed approach is compared with two commercial software packages using real 2D-GE images. The outcome demonstrates that the proposed approach outperforms the two software packages; it detects almost all of real protein spots and a low number of spurious spots.

A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images

cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broad use is that it enables scientists to analyze simultaneously the expression levels of thousands of genes over different samples (Leung et al., 2003). More precisely, the process of a microarray experiment (Campbell et al., 2007) starts with the selection of a set of DNA probes that are of particular interest. A robot places the selected DNA probes on a glass slide, creating an invisible array of DNA dots. Two distinct populations of mRNAs (messenger RNAs) are then isolated from a control sample (i.e a cell developed under normal conditions) and a test sample (i.e. a cell developed under a specific treatment). The mRNA populations are reversely transcribed into cDNA (complementary DNA) populations which in turn are colored with separate fluorescent dyes of different wavelengths (i.e. Cy3 and Cy5). The dyed cDNA populations are mixed with purified water and the solution is placed on the glass slide in order for the cDNA populations to be hybridized with the slide’s DNA dots. Finally, the hybridized glass slide is fluorescently scanned twice; one scan for each dye’s wavelength. Hence, two digital images are produced, one for each population of mRNA. Each digital image contains a number of spots (corresponding to the DNA-cDNA dots) of various fluorescence intensities. Given that the intensity of each spot is proportional to the hybridization level of the cDNAs and the DNA dots, the gene expression information is obtained by analyzing the digital images. As stated by Yang et al (Yang et. al, 2002), the process of analyzing a microarray image can be divided into three main phases, namely: “Gridding”, “Spot-Segmentation” and “SpotIntensity extraction”. During the 1st phase, the microarray image is segmented into numerous compartments, each containing one individual spot and background. During the 2nd phase each compartment is individually segmented into a spot area and a background area, while during the 3rd phase the brightness of each spot is calculated. The expressionlevels of the genes in these spots are a direct result of their individual brightness. Amongst the stages of the microarray-image analysis, spot-segmentation remains the most challenging one. Ideally, the existing spots inside the microarray image are aligned in 2D array layouts. These ‘ideal spots’ also have a circular 2D shape with fixed diameters, while