Biomedical subjects

B Roysam

Publications and source records attributed to B Roysam.

14 recordsLinked to original sources

PubMed1998

Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination.

Although laser retinal surgery is the best available treatment for choridal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motion-compensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for 1) rapid identification of branching and crossover points of the retinal vasculature; 2) automatic montaging of video retinal angiograms; 3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16-30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6-4 s for merging 5-12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus.

Algorithms↗

PubMed1998

Accuracy of nuclear classification in cervical smear images. Quantitative impact of computational deconvolution and 3-D feature computation.

OBJECTIVE: To investigate the accuracy with which the nuclei of cells in overlapped and thick clusters in cervical/ vaginal smears can be classified independent of the segmentation algorithm used and to determine the influence of three-dimensional (3-D) processing as compared to two-dimensional (2-D) methods on classification of the nuclei. STUDY DESIGN: Cell clusters were imaged from 31 ThinPrep smears composed of 808 nuclei, of which 420 were determined to be abnormal by a cytotechnologist. Sets of 2-D and 3-D volumetric features of the detected nuclei were formulated, and classifiers were constructed. The effect of computational deconvolution on classification was assessed using nearest-neighbor and Wiener filter in 2-D and 3-D before calculating features. A "best focus plane" was calculated for each nucleus from the 3-D data set, and the 2-D features in this plane were also analyzed.

Algorithms↗

PubMed1998

Advances in high-speed, three-dimensional imaging and automated segmentation algorithms for thick and overlapped clusters in cytologic preparations. Application to cervical smears.

OBJECTIVE: To use three-dimensional (3-D) imaging and localized adaptive image analysis to enable automated cervical smear screening systems to efficiently and effectively process thick and overlapped cell clusters currently left unprocessed. STUDY DESIGN: Instrumentation was developed to perform high-speed (50-200 optical sections per second at 256 x 256 resolution), 3-D imaging of thick regions of cervical smears. Normal and abnormal ThinPrep smears were imaged at two levels of resolution to approximate higher-resolution, wide-area imaging. Improved dual-resolution, 3-D image analysis algorithms were developed for segmenting nuclei in these clusters. RESULTS: Despite low contrast, high variability and dense overlaps, the algorithms detected 89% and correctly segmented 76% of nuclei in clusters from normal smears and detected 75% and correctly segmented 45% of nuclei in clusters from abnormal smears in low-resolution images. In high-resolution images they detected 88% and segmented 76% of nuclei from normal specimens and detected 55% and segmented 45% of nuclei from abnormal specimens. At least one nucleus from each cell cluster was correctly segmented. CONCLUSION: Selective application of 3-D imaging and 3-D image analysis to thick and overlapped regions can enable a significant fraction (45-89%) of clustered and embedded cells to be accessed by an automated analysis system. These regions are, for the most part, unprocessable by current two-dimensional methods.

Algorithms↗

PubMed1996

Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy.

Automated three-dimensional (3-D) image analysis methods are presented for rapid and effective analysis of populations of fluorescently labeled cells or nuclei in thick tissue sections that have been imaged three dimensionally using a confocal microscope. The methods presented here greatly improve upon our earlier work (Roysam et al.:J Microsc 173: 115-126, 1994). The principal advances reported are: algorithms for efficient data pre-processing and adaptive segmentation, effective handling of image anisotrophy, and fast 3-D morphological algorithms for separating overlapping or connected clusters utilizing image gradient information whenever available. A particular feature of this method is its ability to separate densely packed and connected clusters of cell nuclei. Some of the challenges overcome in this work include the efficient and effective handling of imaging noise, anisotrophy, and large variations in image parameters such as intensity, object size, and shape. The method is able to handle significant inter-cell, intra-cell, inter-image, and intra-image variations. Studies indicate that this method is rapid, robust, and adaptable. Examples were presented to illustrate the applicability of this approach to analyzing images of nuclei from densely packed regions in thick sections of rat liver, and brain that were labeled with a fluorescent Schiff reagent.

Algorithms↗

PubMed1996

Automated 3-D montage synthesis from laser-scanning confocal images: application to quantitative tissue-level cytological analysis.

This paper presents a landmark based method for efficient, robust, and automated computational synthesis of high-resolution, two-dimensional (2-D) or three-dimensional (3-D) wide-area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, and is called the "montage." This technique is used not only to produce gray-level montages, but also to montage the results of automated image analysis, such as 3-D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide-area image at high resolution. The method is based on computing a concise set of feature-tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a first step towards high-resolution large-scale quantitative tissue studies. A detailed example using 3-D laser-scanning confocal microscope images of acriflavine-stained hippocampal sections of rat brain is presented to illustrate the method.

Animals↗

PubMed1994

Three-dimensional imaging and image analysis of hippocampal neurons: confocal and digitally enhanced wide field microscopy.

The microscopy of biological specimens has traditionally been a two-dimensional imaging method for analyzing what are in reality three-dimensional (3-D) objects. This has been a major limitation of the application of one of science's most widely used tools. Nowhere has this limitation been more acute than in neurobiology, which is dominated by the necessity of understanding both large- and small-scale 3-D anatomy. Fortunately, recent advances in optical instrumentation and computational methods have provided the means for retrieving the third dimension, making full 3-D microscopic imaging possible. Optical designs have concentrated on the confocal imaging mode while computational methods have made 3-D imaging possible with wide field microscopes using deconvolution methods. This work presents a brief review of these methods, especially as applied to neurobiology, and data using both approaches. Specimens several hundred micrometers thick can be sampled allowing essentially intact neurons to be imaged. These neurons or selected components can be contrasted with either fluorescent, absorption, or reflection stains. Image analysis in 3-D is as important as visualization in 3-D. Automated methods of cell counting and analysis by nuclear detection as well as tracing of individual neurons are presented.

Animals↗

PubMed1994

Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data.

Three-dimensional (3-D) image analysis algorithms and experimental results that demonstrate the feasibility of fully automated tracing of neurons from fluorescence confocal microscopy data are presented. The input to the automated analysis is a set of successive optical slices that have been acquired using a confocal scanning laser microscope. The output of the system is a labelled graph representation of the neuronal topology that is spatially aligned with the 3-D image data. A variety of topological and metric analyses can be carried out using this representation. For instance, precise measurements of volumes, lengths, diameters and tortuosities can be made over specific portions of the neuron that are specified in terms of the graph representation. The effectiveness of the method is demonstrated for a set of sample fields featuring selectively stained neurons. Additional work will be needed to refine the method for unsupervised use with complex data involving multiple intertwined neurons and extremely fine dendritic structures.

Algorithms↗

PubMed1994

Algorithms for automated characterization of cell populations in thick specimens from 3-D confocal fluorescence microscopy data.

Methods are presented for the automated, quantitative and three-dimensional (3-D) analysis of cell populations in thick, essentially intact tissue sections while maintaining intercell spatial relationships. This analysis replaces current manual methods which are tedious and subjective. The thick sample is imaged in three dimensions using a confocal scanning laser microscope. The stack of optical slices is processed by a 3-D segmentation algorithm that separates touching and overlapping structures using localization constraints. Adaptive data reduction is used to achieve computational efficiency. A hierarchical cluster analysis algorithm is used automatically to characterize the cell population by a variety of cell features. It allows automatic detection and characterization of patterns such as the 3-D spatial clustering of cells, and the relative distributions of cells of various sizes. It also permits the detection of structures that are much smaller, larger, brighter, darker, or differently shaped than the rest of the population. The overall method is demonstrated for a set of rat brain tissue sections that were labelled for tyrosine hydroxylase using fluorescein-conjugated antibodies. The automated system was verified by comparison with computer-assisted manual counts from the same image fields.

Algorithms↗

PubMed1993

Developments in three-dimensional stereo brightfield microscopy.

We present recent developments of a widefield computer/microscope system and image reconstruction algorithm for producing three-dimensional (3D) increased depth of field images in the form of brightfield stereo pairs of thick specimens. The theoretical principle of this image reconstruction technique is based on Weiner-type inverse filtering. A number of extensions and refinements to our previous work have included further testing of the system with a broader class of specimens and the implementation of several pragmatic refinements important for future 3D microscopy systems. These refinements include histogram modification routines for improving visualization, a preprocessing routine to eliminate edge artifacts due to circular convolution and other effects, stereo viewing angle optimization, a rule of thumb estimate for the axial sampling rate, and incorporation of a variation of the Fast Fourier Transform and filtering operations that significantly reduce computational time. Images of spyrogyra, neonatal rat hippocampal neurons, and cervical/vaginal cell smears are presented to show the utility of these methods for 3D visualization. The primary advantages of these methods are that they operate with an ordinary transmitted light microscope and are inexpensively implemented on a personal computer with reasonable computation time.

Algorithms↗

PubMed1993

Iterative, constrained 3-D image reconstruction of transmitted light bright-field micrographs based on maximum likelihood estimation.

We present several image reconstruction algorithms for generating three-dimensional (3-D) renderings of bright-field micrographs that are founded on maximum likelihood estimation (MLE) theory. The basic principle of the algorithms is in estimating the values of the optical densities of the specimen. A computer simulation and initial experimental testing of a steepest ascent version of the algorithm is presented. The computer simulation demonstrates that the MLE algorithm has an advantage over previously used inverse filtering techniques in that it partially restores the zeroed Fourier components in the well-known missing-cone region. We present 3-D reconstructions from real biological data to show the potential of the algorithm in practical applications.

Algorithms↗

PubMed1993

Automated three-dimensional image analysis of thick and overlapped clusters in cytologic preparations. Application to cytologic smears.

Methods are presented for automated analysis of thick and heavily overlapped regions of cytologic preparations, such as cervical/vaginal smears. Current systems are unable to process these regions although they contain diagnostically valuable information. We argue that analysis of such regions is inherently a three-dimensional (3-D) problem that cannot be solved reliably with conventional two-dimensional methods. Furthermore, this issue cannot be side-stepped by special thin preparation methods. Even with 3-D imaging, analysis of these regions is complicated by the high variability in the image gray level and textural features resulting from the uncontrollable cell overlaps and folds and large computational requirements. A novel approach based on 3-D imaging and adaptive 3-D analysis algorithms based on the principles of localization, adaptive data reduction and clustering theory is presented. It was successful in detecting and separating deeply embedded and overlapping nuclei, cytoplasmic folds and creases in thick and overlapped regions of conventional smears and special thin preparations.

Cytological Techniques↗

PubMed1992

A personal computer based implementation of the maximum-likelihood method of analysis of electron microscope autoradiographs.

The maximum-likelihood (ML) method for the quantitative analysis of electron-microscopic autoradiographs has been shown to be substantially superior to the conventional crossfire (CF) method. It can generate reliable and accurate tracer concentration estimates with far fewer micrographs and produce valid estimates even at counts low enough to preclude the use of the crossfire method while eliminating the need for special ad hoc treatment of narrow membranous structures as well as the secondary verification of the tracer concentration estimates. Despite these significant advantages, the large computational requirements of the ML method has to date hampered its widespread use. In this paper, we present a new line-integration method that allows us to reduce the computational requirements of the ML method to a point where it becomes feasible to implement it on a small computer system of the type typically available to a laboratory user of EM autoradiography. We present the complete line-integration method for the particular case of EM autoradiography with tritium, and show how it can be adapted to other isotopes. We have constructed a software package that implements the complete maximum-likelihood method on the IBM PC class of machines using our line-integration method. Features of this software package which are of particular importance to the research community are device independence, which makes it usable with a large variety of currently available laboratory equipment, and easy portability of the software and data between different computer systems.

Algorithms↗

PubMed1991

Bayesian image reconstruction for emission tomography incorporating Good's roughness prior on massively parallel processors.

Since the introduction by Shepp and Vardi [Shepp, L. A. & Vardi, Y. (1982) IEEE Trans. Med. Imaging 1, 113-121] of the expectation-maximization algorithm for the generation of maximum-likelihood images in emission tomography, a number of investigators have applied the maximum-likelihood method to imaging problems. Though this approach is promising, it is now well known that the unconstrained maximum-likelihood approach has two major drawbacks: (i) the algorithm is computationally demanding, resulting in reconstruction times that are not acceptable for routine clinical application, and (ii) the unconstrained maximum-likelihood estimator has a fundamental noise artifact that worsens as the iterative algorithm climbs the likelihood hill. In this paper the computation issue is addressed by proposing an implementation on the class of massively parallel single-instruction, multiple-data architectures. By restructuring the superposition integrals required for the expectation-maximization algorithm as the solutions of partial differential equations, the local data passage required for efficient computation on this class of machines is satisfied. For dealing with the "noise artifact" a Markov random field prior determined by Good's rotationally invariant roughness penalty is incorporated. These methods are demonstrated on the single-instruction multiple-data class of parallel processors, with the computation times compared with those on conventional and hypercube architectures.

Algorithms↗

PubMed1987

Data acquisition system for maximum-likelihood analysis of electron microscopic autoradiographs.

EMAMAP is a program for the data acquisition phase of maximum-likelihood analysis of electron microscope autoradiographs. This program is written in C and has been implemented on a Masscomp MC-500 which supports a graphics processor and a digitizing tablet. The image analysis is automated at a low level: the program operator outlines the edges of the structures of interest using the digitizing tablet, while contiguous regions formed by closed contours are automatically filled by the software. The resulting image is compressed for efficient storage by a quadtree encoding technique for which data compression ratios of greater than 25:1 have been achieved. In practical terms, this implies that the data from a typical experiment of 50 autoradiographs could be stored on a single floppy disk. The system is currently in use for acquiring actual biological experimental data.

Autoradiography↗