Biomedical subjects

B Roysam

Publications and source records attributed to B Roysam.

22 records · Page 2Linked to original sources

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↗