Study of edge detection task in dental panoramic radiographs
Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic
Typ dokumentu srovnávací studie, časopisecké články, práce podpořená grantem
PubMed
23640989
PubMed Central
PMC3699971
DOI
10.1259/dmfr.20120391
PII: dmfr.20120391
Knihovny.cz E-zdroje
- Klíčová slova
- computer-assisted image processing, medical imaging, panoramic radiography,
- MeSH
- algoritmy MeSH
- analýza hlavních komponent MeSH
- anatomická značka diagnostické zobrazování MeSH
- artefakty MeSH
- časové faktory MeSH
- cysty čelistí diagnostické zobrazování MeSH
- extrakce zubů MeSH
- lidé MeSH
- normální rozdělení MeSH
- počítačové zpracování obrazu statistika a číselné údaje MeSH
- pravděpodobnost MeSH
- rentgendiagnostika panoramatická statistika a číselné údaje MeSH
- rentgenový obraz - interpretace počítačová metody MeSH
- zubní kaz diagnostické zobrazování MeSH
- zuby přespočetné diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
The purpose of this study is (1) to introduce a new approach for edge detection in orthopantograms (OPGs) and an improved automatic parameter selector for common edge detectors, (2) to present a comparison between our novel approach with common edge detectors and (3) to provide faster outputs without compromising quality. A new approach for edge detection based on statistical measures was introduced: (1) a set of N edge detection results is calculated from a given input image and a selected type of edge detector, (2) N correspondence maps are constructed from N edge detection results, (3) probabilities and average probabilities are computed, (4) an overall correspondence is evaluated for each correspondence map and (5) the correspondence map providing the best overall correspondence is taken as the result of edge detection procedure. A comparison with common edge detectors (the Roberts, Prewitt, Sobel, Laplacian of the Gaussian and Canny methods) with various parameter settings (304 combinations for each test image) was carried out. The methods were assessed objectively [edge mismatch error (EME), modified Hausdorff distance (MHD) and principal component analysis] and subjectively by experts in dentistry and based on time demands. The suitability of the new approach for edge detection in OPGs was confirmed by experts. The current conventional methods in edge detection in OPGs are inadequate (none of the tested methods reach an EME value or MHD value below 0.1). Our proposed approach for edge detection shows promising potential for its implementation in clinical dentistry. It enhances the accuracy of OPG interpretation and advances diagnosis and treatment planning.
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