@inproceedings{MTMT:2957978, title = {Automatic Brain Tumor Segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm}, url = {https://m2.mtmt.hu/api/publication/2957978}, author = {Szilágyi, László and Lefkovits, László and Benyó, Balázs István}, booktitle = {12th International Conference on Fuzzy Systems and Knowledge Discovery}, doi = {10.1109/FSKD.2015.7381955}, unique-id = {2957978}, keywords = {Magnetic Resonance Imaging; INFORMATION; Image segmentation; inhomogeneity; MAGNETIC-RESONANCE IMAGES; ROBUST; MEANS CLUSTERING-ALGORITHM; semi-supervised clustering; fuzzy c-means algorithm; tumor detection}, year = {2015}, pages = {285-291}, orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127} } @article{MTMT:2698866, title = {Generalization rules for the suppressed fuzzy c-means clustering algorithm}, url = {https://m2.mtmt.hu/api/publication/2698866}, author = {Szilágyi, László and Szilágyi, Sándor Miklós}, doi = {10.1016/j.neucom.2014.02.027}, journal-iso = {NEUROCOMPUTING}, journal = {NEUROCOMPUTING}, volume = {139}, unique-id = {2698866}, issn = {0925-2312}, abstract = {Intending to achieve an algorithm characterized by the quick convergence of hard c-means (HCM) and finer partitions of fuzzy c-means (FCM), suppressed fuzzy c-means (s-FCM) clustering was designed to augment the gap between high and low values of the fuzzy membership functions. Suppression is produced via modifying the FCM iteration by creating a competition among clusters: for each input vector, lower degrees of membership are proportionally reduced, being multiplied by a previously set constant suppression rate, while the largest fuzzy membership grows to maintain the probabilistic constraint. Even though so far it was not treated as an optimal algorithm, it was employed in a series of applications, and reported to be accurate and efficient in various clustering problems. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Further on, we identify the close relation between s-FCM clustering models and the so-called FCM algorithm with generalized improved partition (GIFP-FCM). Finally we reveal the constraints under which the generalized s-FCM clustering models minimize the objective function of GIFP-FCM, allowing us to call our suppressed clustering models optimal. Based on a large amount of numerical tests performed in multidimensional environment, several generalized forms of suppression proved to give more accurate partitions than earlier solutions, needing significantly less iterations than the conventional FCM.}, year = {2014}, eissn = {1872-8286}, pages = {298-309} } @article{MTMT:2695297, title = {Lessons to learn from a mistaken optimization}, url = {https://m2.mtmt.hu/api/publication/2695297}, author = {Szilágyi, László}, doi = {10.1016/j.patrec.2013.08.027}, journal-iso = {PATTERN RECOGN LETT}, journal = {PATTERN RECOGNITION LETTERS}, volume = {36}, unique-id = {2695297}, issn = {0167-8655}, abstract = {The fuzzy local information c-means (FLICM) algorithm, introduced by Krinidis and Chatzis (2010), was designed to perform highly accurate segmentation of images contaminated with high-frequency noise. This algorithm includes an extra additive term to the objective function of the fuzzy c-means (FCM), called local descriptor fuzzy factor, allowing the labeling of a pixel to be influenced by its neighbors, thus achieving a filtering effect. Further on, the authors of FLICM claim that their algorithm does not depend on any trade-off parameter, which were present in all previous similar approaches. In this paper we investigate the theoretical foundation of FLICM and reveal some critical issues. First of all, we show that the iterative optimization algorithm proposed for the minimization of the FLICM objective function is not suitable for the given problem, it does not minimize the objective function. Instead of that, FLICM computes an FCM-like partition using distorted distances, according to the local context of each pixel, thus performing a job that is similar to the so-called suppressed fuzzy c-means algorithm existing in the literature. Finally we reveal the presence of a possible trade-off in the definition of the local descriptor fuzzy term, and the necessity of another factor to compensate against the size of the considered neighborhood. Such algorithms can be effective in certain scenarios, which were documented by the authors, but a deep investigation of the limitations would be beneficial.}, year = {2014}, eissn = {1872-7344}, pages = {29-35} } @article{MTMT:2694345, title = {Robust spherical shell clustering using fuzzy-possibilistic product partition}, url = {https://m2.mtmt.hu/api/publication/2694345}, author = {Szilágyi, László}, doi = {10.1002/int.21591}, journal-iso = {INT J INTELL SYST}, journal = {INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS}, volume = {28}, unique-id = {2694345}, issn = {0884-8173}, abstract = {One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. This paper introduces a novel fuzzy-possibilistic shell clustering model aiming at accurate detection of circles, spheres, and multidimensional spheroids in the presence of outlier data. The proposed fuzzy-possibilistic product partition c-spherical shell algorithm (FP3CSS) combines the probabilistic and possibilistic partitions in a qualitatively different way from previous, similar algorithms. The novel mixture partition is able to suppress the influence of extreme outlier data, which gives it net superiority in terms of robustness and accuracy, compared to previous algorithms.}, year = {2013}, eissn = {1098-111X}, pages = {524-539} } @inproceedings{MTMT:3344749, title = {Fuzzy-possibilistic product partition: A novel robust approach to c-means clustering}, url = {https://m2.mtmt.hu/api/publication/3344749}, author = {Szilágyi, László}, booktitle = {Modeling Decisions for Artificial Intelligence}, doi = {10.1007/978-3-642-22589-5_15}, unique-id = {3344749}, abstract = {One of the main challenges in the field of c-means clustering models is creating an algorithm that is both accurate and robust. In the absence of outlier data, the conventional probabilistic fuzzy c-means (FCM) algorithm, or the latest possibilistic-fuzzy mixture model (PFCM), provide highly accurate partitions. However, during the 30-year history of FCM, the researcher community of the field failed to produce an algorithm that is accurate and insensitive to outliers at the same time. This paper introduces a novel mixture clustering model built upon probabilistic and possibilistic fuzzy partitions, where the two components are connected to each other in a qualitatively different way than they were in earlier mixtures. The fuzzy-possibilistic product partition c- means (FP3CM) clustering algorithm seems to fulfil the initial requirements, namely it successfully suppresses the effect of outliers situated at any finite distance and provides partitions of high quality.}, year = {2011}, pages = {150-161} } @inproceedings{MTMT:2618867, title = {MR Brain Image Segmentation Using an Enhanced Fuzzy C-Means Algorithm}, url = {https://m2.mtmt.hu/api/publication/2618867}, author = {Szilágyi, László and Benyó, Zoltán and Szilágyi, Sándor Miklós and Adam, Hatem Salem}, booktitle = {PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4}, doi = {10.1109/IEMBS.2003.1279866}, unique-id = {2618867}, year = {2003}, pages = {724-726} }