Fully automatic classification of breast cancer microarray images

AbstractA microarray image is used as an accurate method for diagnosis of cancerous diseases. The aim of this research is to provide an approach for detection of breast cancer type. First, raw data is extracted from microarray images. Determining the exact location of each gene is carried out using image processing techniques. Then, by the sum of the pixels associated with each gene, the amount of “genes expression” is extracted as raw data. To identify more effective genes, information gain method on the set of raw data is used. Finally, the type of cancer can be recognized via analyzing the obtained data using a decision tree. The proposed approach has an accuracy of 95.23% in diagnosing the breast cancer types

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Analysis-driven lossy compression of DNA microarray images

DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing lossless coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1

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. © 2006 IEEE