MaZda—a software package for image texture analysis PM Szczypiński, M Strzelecki, A Materka, A Klepaczko Computer methods and programs in biomedicine 94 (1), 66-76, 2009 | 829 | 2009 |
A software tool for automatic classification and segmentation of 2D/3D medical images M Strzelecki, P Szczypinski, A Materka, A Klepaczko Nuclear instruments and methods in physics research section A: Accelerators …, 2013 | 289 | 2013 |
Texture and color based image segmentation and pathology detection in capsule endoscopy videos P Szczypiński, A Klepaczko, M Pazurek, P Daniel Computer methods and programs in biomedicine 113 (1), 396-411, 2014 | 147 | 2014 |
Identifying barley varieties by computer vision PM Szczypiński, A Klepaczko, P Zapotoczny Computers and Electronics in Agriculture 110, 1-8, 2015 | 100 | 2015 |
QMaZda—Software tools for image analysis and pattern recognition PM Szczypiński, A Klepaczko, M Kociołek 2017 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2017 | 38 | 2017 |
3D image texture analysis of simulated and real-world vascular trees M Kociński, A Klepaczko, A Materka, M Chekenya, A Lundervold Computer methods and programs in biomedicine 107 (2), 140-154, 2012 | 38 | 2012 |
Computer simulation of magnetic resonance angiography imaging: model description and validation A Klepaczko, P Szczypiński, G Dwojakowski, M Strzelecki, A Materka PLoS One 9 (4), e93689, 2014 | 32 | 2014 |
Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms A Klepaczko, P Szczypiński, A Deistung, JR Reichenbach, A Materka Computer methods and programs in biomedicine 137, 293-309, 2016 | 28 | 2016 |
MaZda–the software package for textural analysis of biomedical images PM Szczypiński, M Strzelecki, A Materka, A Klepaczko Computers in Medical Activity, 73-84, 2009 | 26 | 2009 |
MaZda–a framework for biomedical image texture analysis and data exploration PM Szczypiński, A Klepaczko Biomedical texture analysis, 315-347, 2017 | 25 | 2017 |
Automated modeling of tubular blood vessels in 3D MR angiography images A Materka, M Kociński, J Blumenfeld, A Klepaczko, A Deistung, B Serres, ... 2015 9th International Symposium on Image and Signal Processing and Analysis …, 2015 | 15 | 2015 |
Academic Press: Cambridge PM Szczypinski, A Klepaczko, A Depeursinge, OS Al-Kadi, JR Mitchell MA, USA, 2017 | 14 | 2017 |
Convex hull-based feature selection in application to classification of wireless capsule endoscopic images P Szczypiński, A Klepaczko Advanced Concepts for Intelligent Vision Systems: 11th International …, 2009 | 14 | 2009 |
An intelligent automated recognition system of abnormal structures in WCE images P Szczypiński, A Klepaczko, M Strzelecki Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS …, 2011 | 13 | 2011 |
Preprocessing of barley grain images for defect identification M Kociołek, PM Szczypiński, A Klepaczko 2017 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2017 | 11 | 2017 |
A multi-layer perceptron network for perfusion parameter estimation in DCE-MRI studies of the healthy kidney A Klepaczko, M Strzelecki, M Kociołek, E Eikefjord, A Lundervold Applied Sciences 10 (16), 5525, 2020 | 10 | 2020 |
Simulation of phase contrast angiography for renal arterial models A Klepaczko, P Szczypiński, M Strzelecki, L Stefańczyk Biomedical engineering online 17, 1-29, 2018 | 10 | 2018 |
Selecting texture discriminative descriptors of capsule endpscopy images P Szczypinski, A Klepaczko 2009 Proceedings of 6th International Symposium on Image and Signal …, 2009 | 10 | 2009 |
Healthy kidney segmentation in the dce-Mr images using a convolutional neural network and temporal signal characteristics A Klepaczko, E Eikefjord, A Lundervold Sensors 21 (20), 6714, 2021 | 8 | 2021 |
Correlations between P53 mutation status and texture features of ct images for hepatocellular carcinoma H Wu, X Chen, J Chen, Y Luo, X Jiang, X Wei, W Tang, Y Liu, Y Liang, ... Methods of Information in Medicine 58 (01), 042-049, 2019 | 7 | 2019 |