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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
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001-es BibID:	BIBFORM039399
Első szerző:	Spisák Tamás (programtervező matematikus, informatikus)
Cím:	BrainMOD : 4-dimensional multimodal medical image analysis software / Tamás Spisák, Sándor Attila Kis, Gábor Opposits, Imre Lajtos, László Balkay, Miklós Emri
Dátum:	2012
ISSN:	0968-5243
Megjegyzések:	Purpose of the softwareWithin the Central Nervous System Imaging project (http://www.eniac-csi.org/) of the ENIAC consortium, the need has emerged for a general multimodal visualization platform which facilitates the evaluation of data produced by new enhanced devices developed in the project. Taking advantage of multi-source post-processed data, this software aims to help interpreting complex intra-modal relationships. The modalities involved are PET, MRI, EEG, EIT.According to the project proposal, our purpose was to develop a software for interactive user-friendly 2D and 3D visualization of post-processed multi-modal medical imaging data. Important requirements were to manage dynamic image data and explicitly support the use of various enhanced brain imaging techniques.Methods / ImplementationThe input of the software are MR structural data, fMRI and PET dynamic data and activation maps (GLM, ICA), EEG/EIT based static functional maps and dynamic data, other EEG and fMRI related time series (eg. hemodynamic response functions, independent component analysis time courses), volumes-of-interests of segmentation data and EEG/EIT marker positions. Besides conventional 2D image fusion features, the software provides numerous ways to reveal intra-modal dynamic relationships. Volumes-of-interests can be delineated manually or automatically aided by various segmentation algorithms or brain atlases [1]. Time series curves can be generated from the image data and on these various operations can be performed (eg. resampling, filters, correlation, convolution).Three dimensional surfaces can be reconstructed, visualized and colored by multiple parameters (eg. dynamic functional information).The program is built upon the MultiModal Medical Imaging software library system (www.minipetct.com/m3i) and runs on Windows 7 and Windows Xp operation systems and various Linux distributions. The hardware requirements of the application match the current average PC configurations used in medical image analysis.The software system was implemented in C++.Features illustrated at the exhibitAt the exhibit the features of the software are illustrated by performing a comparsion analysis of EEG-fMRI activation maps vs. resected area and evaluating the overlap between fMRI parametric maps computed by Independent Component Analysis and standard resting-state network templates [2].References[1] T. Spisák, M. Koselák, G. Opposits, S. A. Kis, L. Trón, A. Jakab, E. Berényi, M. Emri, Region management toolkit for atlas-space image processing, MAGMA 24 (S1):543, 2011.[2] Shirer, WR and Ryali, S. and Rykhlevskaia, E. and Menon, V. and Greicius, MD,Decoding subject-driven cognitive states with whole-brain connectivity patterns, Cerebral Cortex, 22(1):158-162, 2012.
Tárgyszavak:	Orvostudományok Elméleti orvostudományok idézhető absztrakt multimodal medical image processing software
Megjelenés:	Magnetic Resonance Materials In Physics Biology And Medicine. - 25 : S1 (2012), p. 619. -
További szerzők:	Kis Sándor Attila (1973-) (fizikus) Opposits Gábor (1974-) (fizikus, szoftver fejlesztő) Lajtos Imre (1986-) (fizikus) Balkay László (1963-) (biofizikus) Emri Miklós (1962-) (fizikus)
Pályázati támogatás:	120209 FP7
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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