Találati lista | Tudóstér

001-es BibID:	BIBFORM075918
035-os BibID:	(WOS)000458515000002 (Scopus)85064672339
Első szerző:	Béres Mónika (képalkotó diagnoszta, eü. mérnökinformatikus)
Cím:	Comparing the reliability of biomedical texture analysis tools on different image types / Béresová M., Forgács A., Bujdosó B., Székely A., Varga J., Berényi E., Balkay L.
Dátum:	2018
ISSN:	1785-8860
Megjegyzések:	The aim of this study was to analyze the reliability of texture index (TI) calculations using two different approaches. First, we calculated texture parameters on synthetically constructed images using four different biomedical software tools (CGITA, InterView Fusion, Matlab, MaZda). Second, we investigated the reliability of texture parameters, particularly how the texture indices diverge between two similar images with substantially different texture. We generated five different heterogeneous synthetic images, thereafter, histogram-based and co-occurrence matrix features were calculated. The co-occurrence based indices were computed after two (8 and 64) different gray scale normalizations. For the reliability test, we compared 22 texture indices using a histological slice of the brain and Michelangelo's painting, and the gray level dependence was also analyzed. The histogram-based parameters of all images and from all software were very similar. Differences were found in the co-occurrence based indices after both gray level image normalizations. The reliability tests showed that from 22 parameters only 5 texture indices changed more than 20%, and at least 64 normalization levels were necessary for acceptable results. Our results underline that in medical multicenter studies it is especially critical to use the same software package. Some parameters do not reliably reflect changes, so texture analysis (TA) should be used with caution.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk texture analysis medical imaging software comparative study co-occurrence matrix
Megjelenés:	Acta Polytechnica Hungarica. - 15 : 7 (2018), p. 29-48. -
További szerzők:	Forgács Attila (1985-) (fizikus) Bujdosó Blanka Székely András (1982-) (radiológus) Varga József (1955-) (fizikus) Berényi Ervin (1964-) (radiológus) Balkay László (1963-) (biofizikus)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI Szerző által megadott URL
Borító:
Saját polcon:

001-es BibID:	BIBFORM070522
Első szerző:	Béres Mónika (képalkotó diagnoszta, eü. mérnökinformatikus)
Cím:	2D and 3D texture analysis to differentiate brain metastases on MR images : proceed with caution / Béresová Monika, Larroza Andrés, Arana Estanislao, Varga József, Balkay László, Moratal David
Dátum:	2018
ISSN:	0968-5243
Megjegyzések:	ObjectiveTo find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA).Materials and methodsPatients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.ResultsFor LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.ConclusionOur results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
Tárgyszavak:	Orvostudományok Egészségtudományok idegen nyelvű folyóiratközlemény külföldi lapban Computer-assisted Image processing Texture analysis Magnetic resonance imaging Brain neoplasms Metastasis Breast cancer Lung cancer
Megjelenés:	Magnetic Resonance Materials In Physics Biology And Medicine. - 31 : 2 (2018), p. 285-294. -
További szerzők:	Larroza, Andrés Arana, Estanislao Varga József (1955-) (fizikus) Balkay László (1963-) (biofizikus) Moratal, David
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: