Multidimensional Image Analysis Research Articles

VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

Multidimensional Imaging of Mammary Gland Development: A Window Into Breast Form and Function.

An in-depth appreciation of organ form and function relies on the ability to image intact tissues across multiple scales. Difficulties associated with imaging deep within organs, however, can preclude high-resolution multidimensional imaging of live and fixed tissues. This is particularly challenging in the mammary gland, where the epithelium lies deeply encased within a stromal matrix. Recent advances in deep-tissue and live imaging methodologies are increasingly facilitating the visualization of complex cellular structures within their native environment. Alongside, refinements in optical tissue clearing and immunostaining methods are enabling 3D fluorescence imaging of whole organs at unprecedented resolutions. Collectively, these methods are illuminating the dynamic biological processes underlying tissue morphogenesis, homeostasis, and disease. This review provides a snapshot of the current and state-of-the-art multidimensional imaging techniques applied to the postnatal mammary gland, illustrating how these approaches have revealed important new insights into mammary gland ductal development and lactation. Continual evolution of multidimensional image acquisition and analysis methods will undoubtedly offer further insights into mammary gland biology that promises to shed new light on the perturbations leading to breast cancer.

Multidimensional Image Analysis for High Precision Radiation Therapy.

High precision radiation therapy (HPRT) has been improved by utilizing conventional image engineering technologies. However, different frameworks are necessary for further improvement of HPRT. This review paper attempted to define the multidimensional image and what multidimensional image analysis is, which may be feasible for increasing the accuracy of HPRT. A number of researches in radiation therapy field have been introduced to understand the multidimensional image analysis. Multidimensional image analysis could greatly assist clinical staffs in radiation therapy planning, treatment, and prediction of treatment outcomes.

XPIWIT--an XML pipeline wrapper for the Insight Toolkit.

The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos. XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/. johannes.stegmaier@kit.edu Supplementary data are available at Bioinformatics online.

Automated multidimensional image analysis reveals a role for Abl in embryonic wound repair.

The embryonic epidermis displays a remarkable ability to repair wounds rapidly. Embryonic wound repair is driven by the evolutionary conserved redistribution of cytoskeletal and junctional proteins around the wound. Drosophila has emerged as a model to screen for factors implicated in wound closure. However, genetic screens have been limited by the use of manual analysis methods. We introduce MEDUSA, a novel image-analysis tool for the automated quantification of multicellular and molecular dynamics from time-lapse confocal microscopy data. We validate MEDUSA by quantifying wound closure in Drosophila embryos, and we show that the results of our automated analysis are comparable to analysis by manual delineation and tracking of the wounds, while significantly reducing the processing time. We demonstrate that MEDUSA can also be applied to the investigation of cellular behaviors in three and four dimensions. Using MEDUSA, we find that the conserved nonreceptor tyrosine kinase Abelson (Abl) contributes to rapid embryonic wound closure. We demonstrate that Abl plays a role in the organization of filamentous actin and the redistribution of the junctional protein β-catenin at the wound margin during embryonic wound repair. Finally, we discuss different models for the role of Abl in the regulation of actin architecture and adhesion dynamics at the wound margin.

Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu’s method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm’s superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.

Extensible visualization and analysis for multidimensional images using Vaa3D

Open-Source 3D Visualization-Assisted Analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. Once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.

Special issue on “Multidimensional Signal and Image Analysis on HealthCare Applications”

The rapid approaching of elderly era has caused the impending needs for applying new technology for elderly and disabled people to improve health conditions and increase the quality of life. Under such a situation, novel bioengineering devices and tools are being developed to gain better understanding of human metabolic, anatomy and functions of organs. On the other hand, chronic diseases cause healthcare to be shared by daily health maintenance, triggering the impending need for intelligence of devices in which preliminary decisions need to be built into the system for providing preliminary recommendations. To achieve the development of novel and intelligent bioengineering and healthcare devices, the techniques of multidimensional signal and image processing and analysis can play one of the major roles. Compared with one dimensional signal analysis, the analysis of multidimensional signals and images provides more information. On the other hand, the high dimensionality of data may also cause large computational cost and, therefore, reduce the feasibility. Consequently, how to extract information for best estimation of health situations from the correlated or uncorrelated signals needs to be addressed. The goal of this special issue is to provide most up-to-date and recent advances of multidimensional signal and image analysis techniques for healthcare applications. This special issue serves as a forum and venue for researchers in academia, clinics and industries working in this area to share their experiences with the readers. For this special issue, we received a total of 13 submissions, each of which has gone through rigorous peer review. Based on the review results, six papers are selected for publication in this special issue, with brief description as follows. In “Enhancement of Blood Vessels in Retinal Imaging Using the Nonsubsampled Contourlet Transform”, Lee et al. propose a modified version of the nonsubsampled

Tracking and Quantitative Analysis of Dynamic Movements of Cells and Particles

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.

THE TEXTURE CODING METHOD OF MULTISPECTRAL DATA BASED ON SPECTRAL SIMILARITY

As the traditional methods of texture extraction can not be adapted to multispectral and hyperspectral data,this paper presents a coding method of multispectral and hyperspectral data based on spectral similarity.With the concept of mapping mode of multispectral and hyperspectral texture,the new algorithm of texture extraction performs coding by the spectral similarity measure so as to reflect the overall objective texture feature.The method extends the algorithm of extracting texture to multidimensional and hyperdimensional spectral image analysis,and gives multiscale texture combination algorithm.Studies show that the coding mapping method of extracting texture is valid and effective.This paper also demonstrates that the texture feature does greatly improve the accuracy and precision of the classification.

High performance computing environment for multidimensional image analysis

BackgroundThe processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications.ResultsWe present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478× speedup.ConclusionOur parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.

Open Microscopy Environment and FindSpots: integrating image informatics with quantitative multidimensional image analysis

Biomedical research and drug development increasingly involve the extraction of quantitative data from digital microscope images, such as those obtained using fluorescence microscopy. Here, we describe a novel approach for both managing and analyzing such images. The Open Microscopy Environment (OME) is a sophisticated open-source scientific image management database that coordinates the organization, storage, and analysis of the large volumes of image data typically generated by modern imaging methods. We describe FindSpots, a powerful image-analysis package integrated in OME that will be of use to those who wish to identify and measure objects within microscope images or time-lapse movies. The algorithm used in FindSpots is in fact only one of many possible segmentation (object detection) algorithms, and the underlying data model used by OME to capture and store its results can also be used to store results from other segmentation algorithms. In this report, we illustrate how image segmentation can be achieved in OME using one such implementation of a segmentation algorithm, and how this output subsequently can be displayed graphically or processed numerically using a spreadsheet.

Analysis of multidimensional biological image data.

Relatively recent strides in noninvasive medical imaging and in vital microscopy have yielded a new form of massive data type: the multi-channel, 3-D, time-course image recording. These multidimensional datasets document dynamic changes within the full volume of a specimen over time, often simultaneously monitoring several different parameters. As a result, visual data collected from a single living specimen can now go beyond the 2-D domain, engaging the viewer’s full capacity to discern changes across space, time, and additional dimensions such as image spectra. A challenge rising out of these advances is to display these data so that the investigator can visualize and interactively explore the recording’s full spatial, temporal. and spectral content, to better understand what cannot be seen directly through the microscope eyepiece. The challenges of multidimensional image analysis are not unique to the biologist or microscopist—space scientists and climatologists have been struggling with these issues for some time in their analysis of atmospheric data. Here we discuss computational approaches to this type of data, including the introduction of a new interdisciplinary effort to develop an effective framework for the analysis of multidimensional image data.

Fast Algorithm for Local Statistics Calculation for N -Dimensional Images

Local mean and variance measures are frequently required in multi-dimensional image analysis. These measures are needed when calculating correlation coefficients for local image matching purposes. Other measures such as skewness and autocorrelation are useful for texture analysis. This paper presents a fast algorithm for calculating these local statistics in a window of an N -dimensional image. The new algorithm, which is called the plunger method, recursively reduces the dimensions of the input N -dimensional image to achieve fast computation. The speed of the algorithm is independent of the window size. Another advantage of the algorithm is that it calculates the local statistics in one pass. Real image tests have been performed.

Multidimensional pulse image processing of chemical structure data

Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17β-estradiol.

Spatial and temporal dynamics of DNA replication sites in mammalian cells.

Fluorescence microscopic analysis of newly replicated DNA has revealed discrete granular sites of replication (RS). The average size and number of replication sites from early to mid S-phase suggest that each RS contains numerous replicons clustered together. We are using fluorescence laser scanning confocal microscopy in conjunction with multidimensional image analysis to gain more precise information about RS and their spatial-temporal dynamics. Using a newly improved imaging segmentation program, we report an average of approximately 1,100 RS after a 5-min pulse labeling of 3T3 mouse fibroblast cells in early S-phase. Pulse-chase-pulse double labeling experiments reveal that RS take approximately 45 min to complete replication. Appropriate calculations suggest that each RS contains an average of 1 mbp of DNA or approximately 6 average-sized replicons. Double pulse-double chase experiments demonstrate that the DNA sequences replicated at individual RS are precisely maintained temporally and spatially as the cell progresses through the cell cycle and into subsequent generations. By labeling replicated DNA at the G1/S borders for two consecutive cell generations, we show that the DNA synthesized at early S-phase is replicated at the same time and sites in the next round of replication.

User-Steered Image Segmentation Paradigms: Live Wire and Live Lane

In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provideeffective controlto the user on the segmentation processwhileit is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to aslive wireandlive lane, for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ``boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary.We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5–2.5 times faster than manual tracing.

Analyzing 3D Images of the Brain

OVERVIEW During the past 5 years, there has been a considerable effort of research in automating the analysis and fusion of multidimensional images, yielding important theoretical and practical new results. These results could now be useful to brain research. Along these lines, and focusing on 3D images of the brain obtained with CT, MRI, SPECT, and PET techniques, I will present a number of methods useful to produce quantitative measurements necessary for an objective analysis of 3D images of the brain. Such methods include segmentation, shape analysis, rigid and elastic registration, fusion of multimodal images, analysis of temporal sequences (4D data), modeling, and matching of digital atlases. A large number of references on these topics can be found in Ayache (1995a,b), and specific examples from our research group in Malandain et al. (1994), Subsol et al. (1995), and Thirion (1995). The following text describes the research tracks to be followed in order to optimize the future exploitation of 3D images of the brain in neuroscience.

Jordan surfaces in simply connected digital spaces

Certain tasks in multidimensional digital image analysis, in particular surface detection and volume estimation, lead us to the study of “surfaces” in the digital environment. It is desirable that these surfaces should have a “Jordan” property analogous to that of simple closed curves in two dimensions-namely, that they should partition the underlying space into an inside and an outside which are disconnected from each other. A version of this property, called near-Jordanness, has been previously defined and has been shown to be useful within a general theory of surfaces in digital spaces. The definition of a near-Jordan surface is global: it demands that all paths from the interior to the exterior cross the surface. This makes it difficult in many practical applications to check whether surfaces are near-Jordan. The work reported in this paper is motivated by the desire for a “local” condition, such that if a surface satisfies this condition at each of its elements, then it is guaranteed to be near-Jordan. In the search for such a condition, we were led to a concept of “simple connectedness” of a digital space, which resembles simple connectedness in ordinary topological spaces. We were then able to formulate the desired local condition for simply connected digital spaces. Many digital spaces, in particular those based on the commonly-studied tessellations of n-dimensional Euclidean space, are shown to be simply connected and thus our theory yields general sufficient conditions for boundaries in binary pictures to be near-Jordan.