CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). AVAILABILITY AND IMPLEMENTATION: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: There is a strong and growing need in the biology research community for accurate, automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered to meet the needs of its growing user base. It is more robust and user friendly, with new algorithms and features to facilitate high-throughput work. ImageJ plugins can now be run within a CellProfiler pipeline. AVAILABILITY AND IMPLEMENTATION: CellProfiler 2.0 is free and open source, available at http://www.cellprofiler.org under the GPL v. 2 license. It is available as a packaged application for Macintosh OS X and Microsoft Windows and can be compiled for Linux. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modern automated microscopes collect digital images at an astonishing pace. Automated image analysis can measure biological phenotypes quantitatively and reliably, and has therefore become a powerful tool for probing a wide variety of biological questions using microscopy. In this tutorial, we acquaint biologists with this important computational field and introduce some basic principles of image analysis, using typical strategies for two-dimensional images of cultured cells in high-throughput screens as the primary example.

Probabilistic data have recently become popular in applications such as scientific and geospatial databases. For images and other spatial datasets, probabilistic values can capture the uncertainty in extent and class of the objects in the images. Relating one such dataset to another by spatial joins is an important operation for data management systems. We consider probabilistic spatial join (PSJ) queries, which rank the results according to a score that incorporates both the uncertainties associated with the objects and the distances between them. We present algorithms for two kinds of PSJ queries: Threshold PSJ queries, which return all pairs that score above a given threshold, and top-k PSJ queries, which return the k top-scoring pairs. For threshold PSJ queries, we propose a plane sweep algorithm that, because it exploits the special structure of the problem, runs in 0(n (log n + k)) time, where n is the number of points and k is the number of results. We extend the algorithms to 2-D data and to top-k PSJ queries. To further speed up top-k PSJ queries, we develop a scheduling technique that estimates the scores at the level of blocks, then hands the blocks to the plane sweep algorithm. By finding high-scoring pairs early, the scheduling allows a large portion of the datasets to be pruned. Experiments demonstrate speed-ups of two orders of magnitude.

The ability to store and query uncertain information is of great benefit to databases that infer values from a set of observations, including databases of moving objects, sensor readings, historical business transactions, and biomedical images. These observations are often inexact to begin with, and even if they are exact, a set of observations of an attribute of an object is better represented by a probability distribution than by a single number, such as a mean. In this paper, we present adaptive, piecewise-linear approximations (APLAs), which represent arbitrary probability distributions compactly with guaranteed quality. We also present the APLA-tree, an index structure for APLAs. Because APLA is more precise than existing approximation techniques, the APLA-tree can answer probabilistic range queries twice as fast. APLA generalizes to multiple dimensions, and the APLA-tree can index multivariate distributions using either one-dimensional or multidimensional APLAs. Finally, we propose a new definition of k-NN queries on uncertain data. The new definition allows APLA and the APLA-tree to answer k-NN queries quickly, even on arbitrary probability distributions. No efficient k-NN search was previously possible on such distributions.

In many domains, the similarity between two images depends on the spatial locations of their features. The earth mover’s distance (EMD), first proposed by Werman et al. [8], measures such similarity. It yields higher-quality image retrieval results than the Lp-norm, quadratic-form distance, and Jeffrey divergence [6], and has also been used for similarity search on contours [3], melodies [7], and graphs [2].

Because images of neurons show interweaved processes from multiple cells, it is hard to determine which pixels belong to each cell, and consequently to analyze the images automatically. To manage these difficulties, we introduce probabilistic segmentation, in which each pixel is assigned a probability of belonging to each cell instead of being categorically assigned to one cell. We propose a randomized algorithm for probabilistic segmentation. The algorithm is based on repeated, intensity-weighted random walks on the image, and leads to improved segmentation quality. Analysis and mining techniques can utilize the more nuanced and complete information that the probabilistic segmentation yields about an image. Such techniques can then compute probabilistic values, which indicate the level of confidence that can be placed in them.

Given a large collection of medical images of several conditions and treatments, how can we succinctly describe the characteristics of each setting? For example, given a large collection of retinal images from several different experimental conditions (normal, detached, reattached, etc.), how can data mining help biologists focus on important regions in the images or on the differences between different experimental conditions? If the images were text documents, we could find the main terms and concepts for each condition by existing IR methods (e.g., tf/idf and LSI). We propose something analogous, but for the much more challenging case of an image collection: We propose to automatically develop a visual vocabulary by breaking images into n /spl times/ n tiles and deriving key tiles ("ViVos") for each image and condition. We experiment with numerous domain-independent ways of extracting features from tiles (color histograms, textures, etc.), and several ways of choosing characteristic tiles (PCA, ICA). We perform experiments on two disparate biomedical datasets. The quantitative measure of success is classification accuracy: Our "ViVos" achieve high classification accuracy (up to 83 %for a nine-class problem on feline retinal images). More importantly, qualitatively, our "ViVos" do an excellent job as "visual vocabulary terms": they have biological meaning, as corroborated by domain experts; they help spot characteristic regions of images, exactly like text vocabulary terms do for documents; and they highlight the differences between pairs of images.

MOTIVATION: Many biological applications require the comparison of large genome strings. Current techniques suffer from high computational and I/O costs. RESULTS: We propose an efficient technique for local alignment of large genome strings. A space-efficient index is computed for one string, and the second string is compared with this index in order to prune substring pairs that do not contain similar regions. The remaining substring pairs are handed to a hash-table-based tool, such as BLAST, for alignment. A dynamic strategy is employed to optimize the number of disk seeks needed to access the hash table. Additionally, our technique provides the user with a coarse-grained visualization of the similarity pattern, quickly and before the actual search. The experimental results show that our technique aligns genome strings up to two orders of magnitude faster than BLAST. Our technique can be used to accelerate other search tools as well. AVAILABILITY: A web-based demo can be found at http://bioserver.cs.ucsb.edu/. Source code is available from the authors on request.