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149 results on '"Klepaczko, Artur"'

1. Texture Analysis Versus Deep Learning in MRI-based Classification of Renal Failure

Author

Klepaczko, Artur, Majos, Marcin, Stefańczyk, Ludomir, Szychowska, Katarzyna, Kurnatowska, Ilona, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Strumiłło, Paweł, editor, Klepaczko, Artur, editor, Strzelecki, Michał, editor, and Bociąga, Dorota, editor

Published
2024
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3. CNN-Based Quantification of Blood Vessels Lumen in 3D Images

Author

Materka, Andrzej, Jurek, Jakub, Kocinski, Marek, Klepaczko, Artur, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mikyška, Jiří, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2023
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4. Cerebral Vessel Segmentation in CE-MR Images Using Deep Learning and Synthetic Training Datasets

Author

Klepaczko, Artur, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mikyška, Jiří, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2023
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5. Deep Convolutional Neural Networks in Application to Kidney Segmentation in the DCE-MR Images

Author

Klepaczko, Artur, Eikefjord, Eli, Lundervold, Arvid, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Paszynski, Maciej, editor, Kranzlmüller, Dieter, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2021
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7. Deformable Mesh for Regularization of Three-Dimensional Image Registration

Author

Szczypiński, Piotr M., Klepaczko, Artur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pietka, Ewa, editor, Badura, Pawel, editor, Kawa, Jacek, editor, and Wieclawek, Wojciech, editor

Published
2019
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8. Functional Kidney Analysis Based on Textured DCE-MRI Images

Author

Kociołek, Marcin, Strzelecki, Michał, Klepaczko, Artur, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pietka, Ewa, editor, Badura, Pawel, editor, Kawa, Jacek, editor, and Wieclawek, Wojciech, editor

Published
2019
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11. Computer Simulation of the SWI Protocol in Nuclear Magnetic Resonance Imaging

Author

Izydorczyk, Grzegorz, Klepaczko, Artur, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chmielewski, Leszek J., editor, Kozera, Ryszard, editor, Shin, Bok-Suk, editor, and Wojciechowski, Konrad, editor

Published
2014
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15. An Intelligent Automated Recognition System of Abnormal Structures in WCE Images

Author

Szczypiński, Piotr, Klepaczko, Artur, Strzelecki, Michał, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Corchado, Emilio, editor, Kurzyński, Marek, editor, and Woźniak, Michał, editor

Published
2011
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17. Automated Segmentation of Endoscopic Images Based on Local Shape-Adaptive Filtering and Color Descriptors

Author

Klepaczko, Artur, Szczypiński, Piotr, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Blanc-Talon, Jacques, editor, Bone, Don, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2010
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18. Local Polynomial Approximation for Unsupervised Segmentation of Endoscopic Images

Author

Klepaczko, Artur, Szczypiński, Piotr, Daniel, Piotr, Pazurek, Marek, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bolc, Leonard, editor, Tadeusiewicz, Ryszard, editor, Chmielewski, Leszek J., editor, and Wojciechowski, Konrad, editor

Published
2010
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19. Combining Evolutionary and Sequential Search Strategies for Unsupervised Feature Selection

Author

Klepaczko, Artur, Materka, Andrzej, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2010
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23. Convex Hull-Based Feature Selection in Application to Classification of Wireless Capsule Endoscopic Images

Author

Szczypiński, Piotr, Klepaczko, Artur, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2009
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32. Segmentación de riñón en imágenes DCE-MRI usando algoritmos de aprendizaje automático

Author

Montalva Subirats, José Miguel, Klepaczko, Artur, Universitat Politècnica de València. Departamento de Ingeniería de la Construcción y de Proyectos de Ingeniería Civil - Departament d'Enginyeria de la Construcció i de Projectes d'Enginyeria Civil, Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials, Espinosa Asensio, Fernando, Montalva Subirats, José Miguel, Klepaczko, Artur, Universitat Politècnica de València. Departamento de Ingeniería de la Construcción y de Proyectos de Ingeniería Civil - Departament d'Enginyeria de la Construcció i de Projectes d'Enginyeria Civil, Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials, and Espinosa Asensio, Fernando

Abstract

[ES] Las aplicaciones de imágenes médicas son interesantes debido a la falta de intervención necesaria, ya que generalmente es un método no invasivo. Nos permiten tener una idea del interior del cuerpo sin preocuparnos por cuestiones como infecciones, tiempo de recuperación, etc. Últimamente, con el aumento de la potencia computacional, cada vez son más populares los métodos de segmentación automática o semiautomática, ya que simplifican la tarea del radiólogo y ayudan a los médicos a tomar decisiones con imágenes de valor añadido. En esta tesis seleccionamos los riñones como el órgano para trabajar. Seleccionamos el método K-means, un algoritmo automático basado en clusterización de las muestras. Se estudiaron 3 volúmenes de 2 pacientes diferentes. Si bien el resultado de las segmentaciones no es óptimo, podemos observar la influencia en el resultado de la segmentación de la cantidad de clústeres arbitrariamente seleccionados y la cantidad de iteraciones (repeticiones) utilizadas. Todo el trabajo analítico fue llevado a cabo en Matlab R2018a. Los resultados mostraron que existe un número variable de grupos donde las 3 partes principales de interés se segmentan óptimamente dependiendo del volumen. Para un volumen mayor necesitaremos una mayor cantidad de clústeres. También vimos que una cantidad baja de conglomerados puede conducir a una discretización deficiente de los diferentes tejidos y una gran cantidad puede llevar a una segmentación excesiva en la que las muestras pertenecientes al mismo grupo se dividen en varios grupos. En cuanto al número de iteraciones, se observó que las iteraciones bajas pueden conducir a segmentaciones no consistentes y, por lo tanto, no se puede asegurar un resultado óptimo. Se encontró que una cantidad de 5 repeticiones / iteraciones es un valor seguro para obtener segmentaciones consistentes., [EN] The medical imaging applications are interesting due to the lack of intervention needed, as it is generally a non-invasive method. They let us have an idea of the interior of the bodies without worrying about issues such as infections, recovering time, etc. Lately with the increase in power of computers, more and more automatic or semiautomatic segmentation methods have become popular as they simplify the task of the radiologist and help doctors take decisions with value added images. In this thesis we selected the kidneys as the organ to work with. We selected the K-means method, an automatic algorithm based on clusterization of the samples. There were studied 3 volumes, the datasets, from 2 different patients. While the result of the segmentations are not optimal, we can observe the influence in the clusterization of the amount of clusters arbitrary selected and the number of iterations (replicates) used. All the analytic work was carried on Matlab R2018a. The results showed that there is variable number of clusters where the 3 main parts of interest are segmented optimally depending on the volume. For a bigger volume we will need a higher number of clusters. Also we saw that a low amount of clusters can lead to a deficient discretization of the different tissues and a high amount can lead to an over segmentation where samples pertaining to the same cluster are split across several clusters. Regarding to the number of replicates, it was observed that low iterations can lead to segmentations not consistent and thus, it cannot be ensured an optimal output. An amount of 5 replicates/iterations was found to be a secure value to obtain consistent clusterizations.

Published
2020

44. CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images

Author

Jurek, Jakub, primary, Pelesz, Mateusz, additional, Losnegard, Are, additional, Reisaeter, Lars, additional, Wojciechowski, Andrzej, additional, Klepaczko, Artur, additional, Halvorsen, Ole J., additional, Beisland, Christian, additional, Kocinski, Marek, additional, Materka, Andrzej, additional, Rorvik, Jarle, additional, and Lundervold, Arvid, additional

Published
2018
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