Multi-modal optical microscopy image analysis and matching techniques for spatially encoded bead-based microarrays
Date of Issue2017-04-19
School of Computer Science and Engineering
Bead encoding is a key problem central to all multiplexed bead-based microarrays. Most existing bead-based microarrays require complicated and costly bead fabrication and/or sophisticated bead decoding hardware to achieve high multiplexing. Recently, the development of spatial bead encoding techniques have opened up the possibility of using pattern matching and image processing to develop highly multiplexed, high-throughput bead-based arrays. In this thesis, we improve the existing spatial bead encoding scheme, and develop a pipeline of computational methods, which allows for automated spatial bead encoding. Six novel computational methods, that automate the proposed bead encoding scheme, are developed in this thesis. The proposed scheme improves the previously reported spatial encoding schemes by making them better suited for use in real world scenarios. In the spatial bead encoding scheme, a sequential bead deposition method is used to capture the identities of the beads in bright or dark field images of the array. While, a second fluorescence image of the array is used to quantify the target analyte concentrations associated with the beads. By aligning the two images using the patterns formed by the beads, both the bead identities (i.e. the target analyte associate with the bead) and the analyte concentrations are decoded. The six computational methods developed in this thesis, are grouped into three categories: bright and dark field image processing, fluorescence image processing, and bead pattern matching. These methods are developed such that they require minimal parameter tuning, and are able to deal with noisy images acquired in uncontrolled environments As part of the bright and dark field image processing we have developed two novel methods: a fully automatic method for detecting the underlying micro-well grid structure in the images, and an unsupervised learning based method to classify the micro-wells as either empty or containing a bead. We show the ability of the proposed methods to deal with extreme amounts of noise and distortions using multiple datasets. While, microarray image gridding is a well-studied problem in the context of fluorescence images of planar microarrays, to the best of our knowledge no methods have yet been developed for processing either bright or dark field images of bead-based microarrays. Unlike in bright field images, where all the beads are visible, in fluorescence images only those beads that are expressed in the sample are visible. Therefore, in fluorescence image analysis the first step is to detect the beads in the image. To this end, we have developed an unsupervised, simultaneous bead detection and segmentation method which uses the coherence in shape and size of the beads to circumvent the need for parameter optimization. The main challenge in this segmentation is the large dynamic range across which the beads are expressed. We have also developed a probabilistic non-linear gridding method that attempts to establish the micro-well grid structure from the detected beads. While similar gridding techniques have been developed in the past, we make two contributions in this regard: first, we put the gridding method in a probabilistic framework, and second, we show that in certain cases it is impossible to ascertain if the correct grid has been detected without using additional information about the physical properties of the array. The final step in the proposed bead encoding scheme is to establishing one-to-one correspondences between the beads detected in the bright field and fluorescence images. We have developed two methods for this matching. When the micro-well grid structure in the fluorescence image is successfully established, the grid information can be used to reduce the matching problem to a binary grid alignment problem. A grid matching method that is very fast and can handle a large number of beads (>50,000) has been developed for this purpose. Finally, to perform matching when the fluorescence image grid is not available, a novel point pattern matching method that is affine invariant and robust to small non-linear distortions has been developed. The point pattern matching method addresses the problem of matching large point sets in the presence of large amounts of outliers and occlusion. The proposed method is evaluated using several real and simulated datasets.