Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

P-HIP: A Multiresolution halftoning algorithm for progressive display

This thesis describes and implements an algorithmic framework for memory efficient, \u27on-the-fly\u27 halftoning in a progressive transmission environment. Instead of a conventional approach which repeatedly reconstructs the continuous tone image from memory and subsequently halftones it for display, the proposed method achieves significant memory efficiency by storing only the halftoned image and updating it in response to additional information received through progressive transmission. Thus the method requires only a single frame-buffer of bits for storage of the displayed binary image and no additional storage is required for the contone data. The additional image data received through progressive transmission is accommodated through in-place updates of the buffer. The method is thus particularly advantageous for high resolution bi-level displays where it can result in significant savings in memory. The proposed framework is implemented using a suitable multi-resolution, multi-level modification of error diffusion that is motivated by the presence of a single binary frame-buffer. Aggregates of individual display bits constitute the multiple output levels at a given resolution. This creates a natural progression of increasing resolution with decreasing bit-depth. Output images are shown to be comparable in terms of quality to those obtained from the conventional Floyd Steinberg error diffusion algorithm

A virtual reality system using the concentric mosaic: Construction, rendering, and data compression

This paper proposes a new image-based rendering (IBR) technique called "concentric mosaic" for virtual reality applications. IBR using the plenoptic function is an efficient technique for rendering new views of a scene from a collection of sample images previously captured. It provides much better image quality and lower computational requirement for rendering than conventional three-dimensional (3-D) model-building approaches. The concentric mosaic is a 3-D plenoptic function with viewpoints constrained on a plane. Compared with other more sophisticated four-dimensional plenoptic functions such as the light field and the lumigraph, the file size of a concentric mosaic is much smaller. In contrast to a panorama, the concentric mosaic allows users to move freely in a circular region and observe significant parallax and lighting changes without recovering the geometric and photometric scene models. The rendering of concentric mosaics is very efficient, and involves the reordering and interpolating of previously captured slit images in the concentric mosaic. It typically consists of hundreds of high-resolution images which consume a significant amount of storage and bandwidth for transmission. An MPEG-like compression algorithm is therefore proposed in this paper taking into account the access patterns and redundancy of the mosaic images. The compression algorithms of two equivalent representations of the concentric mosaic, namely the multiperspective panoramas and the normal setup sequence, are investigated. A multiresolution representation of concentric mosaics using a nonlinear filter bank is also proposed.published_or_final_versio