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At least 469 records · Page 26Linked to original sources
PubMed2004

[Measurement of gradation curve by the digital test pattern method in a computed radiography system].

Computed radiography (CR) has many variations in gradation, and it is difficult to grasp all of them. We produced the Digital Test Pattern, a step chart made based on digital values that was created by personal computer using Binary Editor. Image data input in the CR system is replaced with the Digital Test Pattern data. A gradation curve is measuring for using the processed output image data in the CR system, such that we can determine the relation between input and output. We termed this the Digital Test Pattern method. Output characteristics after gradation processing are obtained by output signal value and input signal value. Input is known beforehand, and change in the gradation processing parameter is understood from the output pixel value. It is also possible to measure a gradation curve directly from photographic density. Measuring the relation of input and output enabled computer simulation of a gradation curve. These studies indicated the effectiveness of our proposed method in terms of accuracy and ease of use.

Image Processing, Computer-Assisted↗
PubMed1993

[A computer system of automated evaluation of structure-activity relationship of chemical mutagens].

A computer automated structure evaluation (CASE) system has been established. The data base of the system consists of more than 2000 chemicals and hundreds of biophores and biophobes identified by CASE. All programs can be run in micro-computer. Thus, entry of a chemical unknown genetoxity will result in the generation of all the possible fragments. On the basis of the presence and/or absence of these descriptors, CASE can predict activity or lack of it. In addition, and independently of the above, CASE also performs spell out, please and comparison program of genetic toxicology. The CASE program can be used to predict the mutagenicity and carcinogenicity of the many untested chemicals in the ambient environment. The sensitivity and specificity of this system all exceeds 90% and therefore the system has a very good-prospect of being used for forecasting.

Carcinogens↗
PubMed1985

Computer systems for laboratory networks and high-performance NMR.

Modern computer technology is significantly enhancing the associated tasks of spectroscopic data acquisition and data reduction and analysis. Distributed data processing techniques, particularly laboratory computer networking, are rapidly changing the scientist's ability to optimize results from complex experiments. Optimization of nuclear magnetic resonance spectroscopy (NMR) and magnetic resonance imaging (MRI) experimental results requires use of powerful, large-memory (virtual memory preferred) computers with integrated (and supported) high-speed links to magnetic resonance instrumentation. Laboratory architectures with larger computers, in order to extend data reduction capabilities, have facilitated the transition to NMR laboratory computer networking. Examples of a polymer microstructure analysis and in vivo 31P metabolic analysis are given. This paper also discusses laboratory data processing trends anticipated over the next 5-10 years. Full networking of NMR laboratories is just now becoming a reality.

Computers↗
PubMed1999

Neural network in communication with medical computer system.

The paper presents a concept of an experimental module designed to recognise spoken utterances that cover a limited range of words indispensable in dialogues with computer medical systems. Research into the recognition of spoken words by a module based on artificial neural network is described. Usefulness of the obtained results for surgery-assisting multimedia systems and for a patient simulator supporting medical education of students in case history-taking and diagnosing is also discussed.

Artificial Intelligence↗
PubMed1992

[A computer system for image feature analysis of chest radiographs in CT-documented interstitial lung diseases].

This paper reports the clinical significance of a computer-analyzing system to detect and characterize interstitial lung diseases in chest radiographs. One hundred and sixty-four ROIs were selected in the right lungs of 41 patients with normal and those of 41 with diffuse interstitial involvement proved by X-ray CT. Selected ROIs were processed by 4-directional Laplacian-Gaussian filtering, binarization, and determination of linear shadows. For quantitative analysis of interstitial shadows, radiographic index, normalized percent-area of shadows in a ROI, was determined and evaluated in the images. Then, the radiographic indices were compared with CT-documented characteristics of interstitial lung shadows. The results were as follows: 1) Abnormal and normal lungs were well differentiated each other by all kinds of the radiographic indices obtained from the images filtered by 4-directional Laplacian-Gaussian filters and from those processed by determination of linear shadows. 2) ROIs with honeycombing shadows and with other interstitial shadows (interstitial changes other than honeycombing and nodulation) shown in CT were differentiated each other by the radiographic indices obtained from the summation image and the vertical directional image processed by determination of linear shadows (p less than .01). However, ROIs with multiple nodular shadows and with other interstitial shadows were not classified by these radiographic indices. These results indicate that this system may be useful for detection and characterization of interstitial diseases in chest radiographs.

Adult↗