M
Marek Kowal
Researcher at University of Zielona Góra
Publications - 47
Citations - 851
Marek Kowal is an academic researcher from University of Zielona Góra. The author has contributed to research in topics: Fault detection and isolation & Image segmentation. The author has an hindex of 14, co-authored 46 publications receiving 720 citations.
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Journal ArticleDOI
Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images
TL;DR: Four different clustering algorithms are tested and compared in the task of fast nuclei segmentation and it is shown that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.
Fault diagnosis and fault tolerant control
Andrzej Obuchowicz,Wieslaw Wajs,Andrzej Pieczyński,Józef Korbicz,Krzysztof Patan,Marek Kowal,Dariusz Ucinski,Jan Maciej Kościelny,Janusz Petrykowski,Andrzej Dyka,Roman Śmierzchalski,Henryk Welfe,Paweł Drzymała,Sławomir Wiak,Rainer Hampel,Frank Drager,Piotr Tatjewski,Maciej Ławryńczuk,Marcin Zych,Krzysztof Warnke,Paweł Raczyński,Józef Lubkiewicz,Dilek Dustegor,Mogens Blanke,Morten Laursen,Paweł Stoch,Krzysztof Rączka,Maciej Kusy,Maciej Hrebień,Rafal Jozefowicz,Didier Maquin,José Ragot,Elom Ayih Domlan,Henrik Niemann,Niels Kjolstad Poulsen,Marcin Witczak,Przemysław Prętki,Mariusz Domżalski,Zdzisław Kowalczuk,Jean-Marie Flaus,Stéphane Ploix,Dominique Sauter,Christophe Aubrun,Shanbin Li,Youmin Zhang,Ryszard Tadeusiewicz,Marek L. Ogiela,Czesław Cempel,Mariusz Patan,Abed Alrahim Yassine,Andrzej Janczak,Ewa Skubalska-Rafajłowicz,Krzysztof Ciupke,Grzegorz Urbanek,Didier Theilliol,Cédric Join,Krzysztof E. Oliński,Piotr M. Marusak,Krzysztof Mazur,Mieczysław Adam Brdyś,Tharrault. Yvon,Gilles Mourot,Gracjan Głowacki,Antoni Lizęga,Maciej Cholewa,M. Gibiec,Adam Pietrzyk,Marcin Andrzejewski,Piotr Przystałka,Łukasz Dziekan,Andrzej Marciniak +70 more
Journal ArticleDOI
Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm
TL;DR: Convolutional neural network outperforms Otsu thresholding and adaptive thresholding in most cases, especially in scenarios with many overlapping nuclei, and the use of a convolutional Neural Network instead of the intensity thresholding to generate a topographical map for the watershed improves segmentation outcomes.
Journal ArticleDOI
Neuro-fuzzy networks and their application to fault detection of dynamical systems
Józef Korbicz,Marek Kowal +1 more
TL;DR: The bounded-error approach is applied to generate rules for the model using available data and the proposed algorithms are applied to fault detection in a valve that is a part of the technical installation at the Lublin sugar factory in Poland.
Book ChapterDOI
Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation
TL;DR: In this paper, a k-means based hybrid segmentation method for breast cancer diagnosis problem is presented, which is part of the computer system to support diagnosis based on microscope images of the fine needle biopsy.