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.
Abstract-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.
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.
Environmental factors affecting nutrient availability during development can have long-term effects including predisposition to diseases later in life. In order to identify chemicals in the environment capable of altering nutrient mobilization, we analyzed yolk malabsorption in the zebrafish embryo, which relies on maternally-derived yolk for nutrition during the first week of life. Embryos of the transgenic zebrafish line HGn50D, which fluoresce in the yolk syncytial layer, the heart, and the eyes of the zebrafish, were exposed from two to five days post fertilization to nine chemicals, including pesticides (prochloraz, imazalil, and butralin), pharmaceuticals (clofibrate and gemfibrozil), flame retardants (tetrabromobisphenol A, and tetrachlorobisphenol A), a surfactant (perfluorooctanoic acid) and a biocide (tributyltin). We developed a software package named ZebRA to automatically and accurately segment and quantify the area of the fluorescing yolk in images captured at the end of the treatment period. Based on this quantification, we found that prochloraz decreased yolk absorption, while butralin, tetrabromobisphenol A, tetrachlorobisphenol A and tributyltin, at the higher concentrations tested, increased yolk absorption. Given the number and variety of industrial chemicals in commerce today, development of automated image processing allowing for high-speed analysis and accurate quantification of biological effects is an important step for enabling high throughput screening to identify chemicals adversely impacting nutrient absorption.
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.
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