Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x

Multi-dimensional arrays are among the most fundamental and most useful data structures of all. In C++, excellent template libraries exist for arrays whose dimension is fixed at runtime. Arrays whose dimension can change at runtime have been implemented in C. However, a generic object-oriented C++ implementation of runtime-flexible arrays has so far been missing. In this article, we discuss our new implementation called Marray, a package of class templates that fills this gap. Marray is based on views as an underlying concept. This concept brings some of the flexibility known from script languages such as R and MATLAB to C++. Marray is free both for commercial and non-commercial use and is publicly available from www.andres.sc/marrayComment: Free source code availabl

How to Extract the Geometry and Topology from Very Large 3D Segmentations

Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments, faces between segments, curves in which several faces meet--as well as a topology on these objects. Existing algorithms encode this information in designated data structures, but require that these data structures fit entirely in Random Access Memory (RAM). Today, 3D images with several billion voxels are acquired, e.g. in structural neurobiology. Since these large volumes can no longer be processed with existing methods, we present a new algorithm which performs geometry and topology extraction with a runtime linear in the number of voxels and log-linear in the number of faces and curves. The parallelizable algorithm proceeds in a block-wise fashion and constructs a consistent representation of the entire volume image on the hard drive, making the structure of very large volume segmentations accessible to image analysis. The parallelized C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.phpComment: C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.ph

The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search

This article presents a new search algorithm for the NP-hard problem of optimizing functions of binary variables that decompose according to a graphical model. It can be applied to models of any order and structure. The main novelty is a technique to constrain the search space based on the topology of the model. When pursued to the full search depth, the algorithm is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to Iterated Conditional Modes. Between these extremes, a useful tradeoff between approximation quality and runtime is established. Experiments on models derived from both illustrative and real problems show that approximations found with limited search depth match or improve those obtained by state-of-the-art methods based on message passing and linear programming.Comment: C++ Source Code available from http://hci.iwr.uni-heidelberg.de/software.ph

amsrpm: Robust Point Matching for Retention Time Aligment of LC/MS Data with R

Proteomics is the study of the abundance, function and dynamics of all proteins present in a living organism, and mass spectrometry (MS) has become its most important tool due to its unmatched sensitivity, resolution and potential for high-throughput experimentation. A frequently used variant of mass spectrometry is coupled with liquid chromatography (LC) and is denoted as "LC/MS". It produces two-dimensional raw data, where significant distortions along one of the dimensions can occur between different runs on the same instrument, and between instruments. A compensation of these distortions is required to allow for comparisons between and inference based on different experiments. This article introduces the amsrpm software package. It implements a variant of the Robust Point Matching (RPM) algorithm that is tailored for the alignment of LC and LC/MS experiments. Problem-specific enhancements include a specialized dissimilarity measure, and means to enforce smoothness and monotonicity of the estimated transformation function. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. Its usefulness is demonstrated using both simulated and experimental data. The software is available as an open source package for the statistical programming language R.