Classification of pixels in a microarray image based on pixel intensities and a preview mode facilitated by pixel-intensity-based pixel classification

Abstract

One disclosed embodiment is a method based on an iteratively employed Bayesian-probability-based pixel classification, used to refine an initial feature mask that specifies those pixels in a region of interest, including and surrounding a feature in the scanned image of a microarray, that together compose a pixel-based image of the feature within the region of interest. In a described embodiment, a feature mask is prepared using only the pixel-based intensity data for a region of interest, a putative position and size of the feature within the region of interest, and mathematical models of the probability distribution of background-pixel and feature-pixel signal noise and mathematical models of the probabilities of finding feature pixels and background pixels at various distances from the putative feature position. In a described embodiment, preparation of a feature mask allows a feature-extraction system to display feature sizes and locations to a user prior to undertaking the computationally intensive and time-consuming task of feature-signal extraction from the pixel-based intensity data obtained by scanning a microarray.

Description

TECHNICAL FIELD [0001] The present invention is related to processing of microarray data and, in particular, to a method and system for partitioning pixels in an image of a microarray into a set of feature pixels and a set of background pixels. BACKGROUND OF THE INVENTION [0002] The present invention is related to methods and systems for determining which pixels, in a digital image of a microarray, are associated with features of the microarray, and which pixels are background pixels associated with inter-feature regions of a microarray. A general background of microarray technology is first provided, in this section, to facilitate discussion of microarray-data processing, in following subsections. It should be noted that microarrays are also referred to as “microarrays” and simply as “arrays.” These alternate terms may be used interchangeably in the context of microarrays and microarray technologies. Art described in this section is not admitted to be prior art to this application....

AI Technical Summary

Benefits of technology

[0014] In one embodiment of the present invention, a method based on an iteratively employed Bayesian-probability-based pixel classification is used to refine an initial feature mask that specifies those pixels in a region of interest, including and surrounding a feature in the scanned image of a microarray, that together compose a pixel-based image of the feature within the region of interest. In the described embodiment, a two-dimensional Boolean array is employed as a feature mask, each cell within the two-dimensional array indicating whether or a corresponding pixel within the pixel-based scanned image of the region of interest surrounding and including the feature is a feature pixel or a background pixel. The feature mask is prepared using the pixel-based intensity data for a region of interest, a putative position and size of the feature within the region of interest, and mathematical models of the probability distribution of background-pixel and feature-pixel signal noise and mathematical models of the probabilities of finding feature pixels and background pixels at various distances from the putative feature position. Preparation of a feature mask allows a feature-extraction system to display feature sizes and locations to a user prior to undertaking the computationally intensive and time-consuming task of feature-signal extraction from the pixel-based intensity data obtained by scanning a microarray.

Problems solved by technology

In a raw and / or partially processed scanned image of a microarray, features may be asymmetrical, may contain highly non-uniform intensities due to a large number of different possible procedural, experimental, and instrumental errors and instabilities, and may be substantially offset from their expected positions within the general grid-like, regular pattern in which features are deposited.

All of these effects can lead to difficulties in employing the currently used, intensity-based feature-pixel / background-pixel partitioning methods described above.

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