cDNA Microarray Basics
cDNA Microarray Imaging
cDNA Image Noise
cDNA Characteristics
cDNA Image Denoising
cDNA Data Normalization
cDNA Spot Localization
cDNA Spot Segmentation


cDNA Spot Segmentation

Image processing techniques such as denoising, enhancement, data normalization, and spot localization, usually precede the image segmentation step which is considered the most important processing operation to be performed prior the determination of the gene expressions. Image segmentation refers to partitioning an image into different regions that are homogeneous with respect to some image feature. Assuming that the pixels with the same feature characteristics constitute meaningful regions such the microarray spots, the problem reduces to pixel classification. Since under the ideal conditions each spot has its own unique magnitude and directional characteristic, uniformity in vector magnitude and directionality can be used as the criterion for partitioning the cDNA vector field into disjoint regions corresponding to distinguishable spots.

Acquired cDNA image


Segmented spots


The segmentation can be performed by achieving root signals of a nonlinear vector filtering operator which uses simultaneously both the magnitude and directional characteristics of the cDNA vectorial inputs during processing and outputs one of the vectorial inputs inside a processing window as the result of the filtering operation. In this way, the filtering process converges within a few iterations to a root signal, which is not further affected by the processing filter. The repetitive use of an operator capable of normalizing the data population emphasizes the most dominant cDNA vectors in localized neighborhoods. Thus, the generated root signal represents the segmented microarray image with the regular spots ideally separated from the background. Moreover, during its convergence phase such a cDNA image processing solution simultaneously denoises, enhances, normalizes data, rejects irregular spots, and automatically segments spots from the background. Combined with an additional module which thresholds the magnitude of the root signal, the approach can remove residual, irregular foreground information and enhance perceived and measured differences between foreground and background information in the segmented image.


bulletR. Lukac and K.N. Plataniotis, "cDNA Microarray Image Segmentation Using Root Signals,"  International Journal of Imaging Systems and Technology, vol. 16, no. 2, pp. 51-64, April 2006.

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Last update: 10/15/06

2006 Rastislav Lukac