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Artur Klepaczko
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2020 – today
- 2024
- [c27]Ewelina Rosiak, Artur Klepaczko:
Transfer learning in application to semantic segmentation of 3D indoor scenes scanned with a depth camera. SPA 2024: 24-29 - [e1]Pawel Strumillo, Artur Klepaczko, Michal Strzelecki, Dorota Bociaga:
The Latest Developments and Challenges in Biomedical Engineering - Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, PCBBE 2023, Lodz, Poland, September 27-29, 2023. Lecture Notes in Networks and Systems 746, Springer 2024, ISBN 978-3-031-38429-5 [contents] - 2023
- [c26]Artur Klepaczko:
Cerebral Vessel Segmentation in CE-MR Images Using Deep Learning and Synthetic Training Datasets. ICCS (4) 2023: 274-288 - [c25]Andrzej Materka
, Jakub Jurek
CNN-Based Quantification of Blood Vessels Lumen in 3D Images. ICCS (2) 2023: 653-661 - [c24]Artur Klepaczko, Marcin Majos, Ludomir Stefanczyk, Katarzyna Szychowska, Ilona Kurnatowska:
Texture Analysis Versus Deep Learning in MRI-based Classification of Renal Failure. PCBBE 2023: 81-93 - 2021
- [j7]Artur Klepaczko
Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics. Sensors 21(20): 6714 (2021) - [c23]Artur Klepaczko
Deep Convolutional Neural Networks in Application to Kidney Segmentation in the DCE-MR Images. ICCS (3) 2021: 609-622
2010 – 2019
- 2019
- [c22]Marcin Kociolek
Functional Kidney Analysis Based on Textured DCE-MRI Images. ITIB 2019: 38-49 - [c21]Piotr M. Szczypinski
Deformable Mesh for Regularization of Three-Dimensional Image Registration. ITIB 2019: 67-78 - 2018
- [c20]Jakub Jurek
CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images. SPA 2018: 174-179 - [c19]Artur Klepaczko
Automated determination of arterial input function in DCE-MR images of the kidney. SPA 2018: 280-285 - [c18]Michal Strzelecki
An artificial neural network for GFR estimation in the DCE-MRI studies of the kidneys. SPA 2018: 286-291 - [c17]Artur Klepaczko
Numerical simulation of the b-SSFP sequence in MR perfusion-weighted imaging of the kidney. SPA 2018: 292-297 - 2017
- [c16]Piotr M. Szczypinski
Barley defects identification. ISPA 2017: 216-219 - [c15]Izabella Korczynska-Czurak, Artur Klepaczko
Vessel tree surface modeling for blood flow simulation. SPA 2017: 111-115 - [c14]Piotr M. Szczypinski
QMaZda - Software tools for image analysis and pattern recognition. SPA 2017: 217-221 - [c13]Marcin Kociolek
Preprocessing of barley grain images for defect identification. SPA 2017: 365-370 - 2016
- [j6]Artur Klepaczko
Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms. Comput. Methods Programs Biomed. 137: 293-309 (2016) - [c12]Artur Klepaczko
Predicting MR angiography image appearance for realistic models of stenosed carotid and renal arteries. SPA 2016: 327-332 - 2015
- [j5]Piotr M. Szczypinski
Identifying barley varieties by computer vision. Comput. Electron. Agric. 110: 1-8 (2015) - [c11]Andrzej Materka
Automated modeling of tubular blood vessels in 3D MR angiography images. ISPA 2015: 54-59 - 2014
- [j4]Piotr M. Szczypinski
Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput. Methods Programs Biomed. 113(1): 396-411 (2014) - [c10]Grzegorz Izydorczyk, Artur Klepaczko:
Computer Simulation of the SWI Protocol in Nuclear Magnetic Resonance Imaging. ICCVG 2014: 294-301 - 2013
- [c9]Artur Klepaczko
A GPU Accelerated Local Polynomial Approximation Algorithm for Efficient Denoising of MR Images. CORES 2013: 419-428 - [c8]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 - 2012
- [j3]Marek Kocinski
3D image texture analysis of simulated and real-world vascular trees. Comput. Methods Programs Biomed. 107(2): 140-154 (2012) - 2011
- [j2]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) - [c7]Piotr M. Szczypinski
An Intelligent Automated Recognition System of Abnormal Structures in WCE Images. HAIS (1) 2011: 140-147 - 2010
- [c6]Artur Klepaczko
Automated Segmentation of Endoscopic Images Based on Local Shape-Adaptive Filtering and Color Descriptors. ACIVS (1) 2010: 245-254 - [c5]Artur Klepaczko
Combining Evolutionary and Sequential Search Strategies for Unsupervised Feature Selection. ICAISC (2) 2010: 149-156 - [c4]Artur Klepaczko
Local Polynomial Approximation for Unsupervised Segmentation of Endoscopic Images. ICCVG (2) 2010: 33-40 - [c3]Piotr M. Szczypinski
Automated Recognition of Abnormal Structures in WCE Images Based on Texture Most Discriminative Descriptors. IP&C 2010: 263-270
2000 – 2009
- 2009
- [j1]Piotr M. Szczypinski
MaZda - A software package for image texture analysis. Comput. Methods Programs Biomed. 94(1): 66-76 (2009) - [c2]Piotr M. Szczypinski
Convex Hull-Based Feature Selection in Application to Classification of Wireless Capsule Endoscopic Images. ACIVS 2009: 664-675 - [p1]Artur Klepaczko, Marek Kocinski
Cluster Analysis in Application to Quantitative Inspection of 3D Vascular Tree Images. Computer Recognition Systems 3 2009: 87-94 - 2005
- [c1]Artur Klepaczko, Andrzej Materka
Feature Selection in Unsupervised Context: Clustering Based Approach. CORES 2005: 219-226
Coauthor Index
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