Search PubMedSearch

PubMed · 3166673

Sampling density and quantitative microscopy.

Abstract

The sampling densities required for the quantitative analysis of digitized microscope images is discussed. It is shown that the Nyquist sampling theorem is not the proper reference point for determining the sampling density when the goal is measurement, although it may be a proper reference point when the goal is image filtering and reconstruction. The problems associated with signal truncation--the use of a finite amount of data--and the finite amount of time available for computation make it impossible to reconstruct an arbitrary image, even if it is bandlimited. Two examples taken from straightforward measurement problems exhibit the fundamental problems associated with the measurement of analog quantities from digital data and the role played by the sampling density.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

I T Young. 1988. Sampling density and quantitative microscopy.. https://pubmed.ncbi.nlm.nih.gov/3166673/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Classification of crossed immunoelectrophoretic patterns using digital image processing and artificial neural networks.

A method is presented which makes it possible to present crossed immunoelectrophoretic patterns to an artificial neural network. The electrophoretic patterns are presented for the artificial neural network as three-dimensional vectors and it is shown that it is possible with this representation to train the network to learn the patterns and classify them. It was found that the ability to generalize was substantially increased by the addition of noise to the input patterns during training. Furthermore, the addition of noise decreased the number of presentations needed to reach the predetermined error level. The trained neural network was able to classify all distorted patterns correctly within an error range of 1%.

Image Processing, Computer-Assisted

Neural network synthesis of spin echo multiecho sequences.

Spin echo multiecho sequences are not frequently used in clinical practice, because they allow the observation of one single slice, imaged at different echo times, for each acquisition. To limit examination time, multislice sequences that include only images derived from one or two echoes are usually acquired. Nevertheless, the strong T2 dependence of multiecho sequences can be used effectively to enhance the contrast between tissues with different T2 and to gather useful diagnostic information. Artificial neural networks can offer new interesting facilities to the radiologist. In fact, the learning capabilities of neural networks allow them to extract the prototypical behavior of a system from a set of examples. After learning, artificial neural networks can emulate the system behavior even in the presence of new inputs, as far as these are not too different from those included in the training set. A conveniently trained neural network can synthesize a multiecho sequence for each slice of a multislice sequence, requiring only two images for each slice to achieve reliable results. When compared with a true multiecho sequence, the images generated by the network preserve the contrast characteristics of the original ones and have a better signal-to-noise (SNR) ratio. In this paper we report the results achieved by using a neural network to reconstruct synthetic spin echo multiecho images of the brain.

Image Processing, Computer-Assisted

Oral diagnostic reporting and synchronized image filing using magneto-optical disks.

An experimental radiologic reporting system has been developed and tested. The rewritable and compact magneto-optical disk (MOD) is applied to storing medical images with oral diagnostic reports of these radiologic images. The disk is 5.25 inches in diameter, has 600 MB memory capacity, is erasable, light and compact. Advantages are simultaneous recording of radiologic images and their oral reports by radiologists, and application to circulation of media inside the hospital as well as to filing of medical images. The MOD has a multimedia function of communication and filing. When medical images are taken and stored, oral interpretation by radiologists can be simultaneously added. Physicians can get information of the images and their reports by oral speech at the same time in front of computer workstation. Furthermore, integration of a voice recognition capability is now being undertaken.

Image Processing, Computer-Assisted