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Andrzej Materka
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2020 – today
- 2024
- [j10]Andrzej Materka
, Jakub Jurek
Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images. Sensors 24(3): 846 (2024) - [i2]Jakub Jurek, Andrzej Materka, Kamil Ludwisiak, Agata Majos, Filip Szczepankiewicz:
MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images. CoRR abs/2410.16898 (2024) - 2023
- [c26]Jakub Jurek
Phase Correction and Noise-to-Noise Denoising of Diffusion Magnetic Resonance Images Using Neural Networks. ICCS (2) 2023: 638-652 - [c25]Andrzej Materka
CNN-Based Quantification of Blood Vessels Lumen in 3D Images. ICCS (2) 2023: 653-661 - [c24]Jakub Jurek
Improving the Resolution and SNR of Diffusion Magnetic Resonance Images From a Low-Field Scanner. PCBBE 2023: 147-160 - 2022
- [c23]Andrzej Materka:
Blood-vessels lumen geometric modeling and quantification from 3D images. SPA 2022: 15 - [i1]Jakub Jurek, Andrzej Materka, Kamil Ludwisiak, Agata Majos, Kamil Gorczewski, Kamil Cepuch, Agata Zawadzka:
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning. CoRR abs/2206.00305 (2022) - 2021
- [j9]Frank G. Zöllner
Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects. IEEE Access 9: 71577-71605 (2021) - 2020
- [c22]Jakub Jurek
On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers - A Simulation Study. ICCVG 2020: 72-86
2010 – 2019
- 2018
- [c21]Michal Strzelecki
On the Influence of Image Features Wordlength Reduction on Texture Classification. ITIB 2018: 15-26 - [c20]Jakub Jurek
Dictionary-based through-plane interpolation of prostate cancer T2-weighted MR images. SPA 2018: 168-173 - [c19]Jakub Jurek
CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images. SPA 2018: 174-179 - [c18]Carlos Vinhais, Marek Kocinski
Centerline-Radius Polygonal-Mesh Modeling of Bifurcated Blood Vessels in 3D Images using Conformal Mapping. SPA 2018: 180-185 - 2017
- [c17]Marek Kocinski
Towards multi-scale personalized modeling of brain vasculature based on magnetic resonance image processing. IWSSIP 2017: 1-5 - [c16]Marek Kocinski
On accuracy of personalized 3D-printed MRI-based models of brain arteries. IWSSIP 2017: 1-5 - [c15]Piotr Zasinski, Marek Kocinski
On extracting skeletons from binary 3D images. IWSSIP 2017: 1-5 - [c14]Jakub Jurek
Rule-based data-driven approach for computer aided diagnosis of the peripheral zone prostate cancer from multiparametric MRI: Proof of concept. SPA 2017: 90-95 - [c13]Jakub Jurek
Reconstruction of high-resolution T2W MR images of the prostate using maximum a posteriori approach and Markov random field regularization. SPA 2017: 96-99 - 2016
- [j8]Artur Klepaczko
Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms. Comput. Methods Programs Biomed. 137: 293-309 (2016) - [c12]Marek Kocinski
Centerline-based surface modeling of blood-vessel trees in cerebral 3D MRA. SPA 2016: 85-90 - [c11]Marek Kocinski
Texture analysis of 2D spatial distribution of blood pharmacokinetic model parameters for endometrial carcinoma classification. SPA 2016: 91-96 - 2015
- [c10]Andrzej Materka
Automated modeling of tubular blood vessels in 3D MR angiography images. ISPA 2015: 54-59 - [c9]Jacek Blumenfeld, Marek Kocinski
A centerline-based algorithm for estimation of blood vessels radii from 3D raster images. SPA 2015: 38-43 - 2014
- [j7]Pawel Poryzala
Cluster analysis of CCA coefficients for robust detection of the asynchronous SSVEPs in brain-computer interfaces. Biomed. Signal Process. Control. 10: 201-208 (2014) - [c8]Artur Wujcicki, Andrzej Materka
Quantitative and Qualitative Evaluation of Selected Lung MR Image Registration Techniques. ICCVG 2014: 653-660 - 2013
- [c7]Grzegorz Dwojakowski, Artur Klepaczko
Blood Flow Modeling in a Synthetic Cylindrical Vessel for Validating Methods of Vessel Segmentation in MRA Images. CORES 2013: 601-610 - [c6]Andrzej Materka
High-speed noninvasive brain-computer interfaces. HSI 2013: 7-12 - 2012
- [j6]Marek Kocinski
3D image texture analysis of simulated and real-world vascular trees. Comput. Methods Programs Biomed. 107(2): 140-154 (2012) - 2011
- [j5]Artur Klepaczko
Quantitative description of 3D vascularity images: texture-based approach and its verification through cluster analysis. Pattern Anal. Appl. 14(4): 415-424 (2011) - [c5]Marcin Byczuk, Pawel Poryzala
On Possibility of Stimulus Parameter Selection for SSVEP-Based Brain-Computer Interface. ICMMI 2011: 57-64 - 2010
- [c4]Artur Klepaczko
Combining Evolutionary and Sequential Search Strategies for Unsupervised Feature Selection. ICAISC (2) 2010: 149-156
2000 – 2009
- 2009
- [j4]Piotr M. Szczypinski
MaZda - A software package for image texture analysis. Comput. Methods Programs Biomed. 94(1): 66-76 (2009) - [p1]Artur Klepaczko, Marek Kocinski
Cluster Analysis in Application to Quantitative Inspection of 3D Vascular Tree Images. Computer Recognition Systems 3 2009: 87-94 - 2006
- [j3]Michal Strzelecki
Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms. Comput. Medical Imaging Graph. 30(2): 95-107 (2006) - 2005
- [c3]Artur Klepaczko, Andrzej Materka
Feature Selection in Unsupervised Context: Clustering Based Approach. CORES 2005: 219-226 - 2004
- [c2]Michal Strzelecki, Andrzej Materka
On the accuracy of Selected Image Texture Segmentation Methods. ICCVG 2004: 546-551
1990 – 1999
- 1996
- [j2]Andrzej Materka
Parametric testing of mixed-signal circuits by ANN processing of transient responses. J. Electron. Test. 9(1-2): 187-202 (1996) - [j1]Andrzej Materka, Shizuo Mizushina:
Parametric signal restoration using artificial neural networks. IEEE Trans. Biomed. Eng. 43(4): 357-372 (1996) - 1995
- [c1]Ian Kaminskyj, Andrzej Materka
Automatic source identification of monophonic musical instrument sounds. ICNN 1995: 189-194
Coauthor Index
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