Journal article
An automated intelligent diagnostic system for the interpretation of umbilical artery Doppler velocimetry
European journal of radiology, v 23(2), pp 162-167
1996
PMID: 8886731
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The objective is to develop an automated intelligent diagnostic system for the interpretation of umbilical artery velocity waveforms. An ultrasound instrument with pulsed-wave Doppler is connected to a microcomputer by means of a frame grabber. After data acquisition, umbilical Doppler velocimetry is handled as a pattern recognition (feature extraction and classification) and decision-making problem. Automated image processing (enhancement, smoothing/thresholding and edge detection) and analysis are used for feature extraction. Six waveform indices obtained by feature extraction are used as input layer to vector quantization which classifies waveforms into six groups. A clinical decision is assigned to each group by the medical expert. Our system is trained by 278 and 380 waveform images of 94 normal and 157 high risk pregnancies, respectively. The system was tested with 193 and 61 images of normal and risky pregnancies; it was demonstrated that sensitivity and specificity of the system are 54.1% and 80.3%, respectively.
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Details
- Title
- An automated intelligent diagnostic system for the interpretation of umbilical artery Doppler velocimetry
- Creators
- M.Sinan Beksaç - Hacettepe UniversityAli Egemen - Hacettepe UniversityKurtuluş izzetoǵlu - Middle East Technical UniversityGülşah Ergün - Hacettepe UniversityAydan M. Erkmen - Middle East Technical University
- Publication Details
- European journal of radiology, v 23(2), pp 162-167
- Publisher
- Elsevier Ireland Ltd
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:A1996VJ95500018
- Scopus ID
- 2-s2.0-0030245760
- Other Identifier
- 991019231641704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Radiology, Nuclear Medicine & Medical Imaging