TY - CHAP AU - Szilágyi, László AU - Lefkovits, László AU - Benyó, Balázs István ED - Zhuo, Tang ED - Jiayi, Du ED - Shu, Yin ED - Ligang, He ED - Renfa, Li TI - Automatic Brain Tumor Segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm T2 - 12th International Conference on Fuzzy Systems and Knowledge Discovery PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York, New York SN - 9781467376822 PY - 2015 SP - 285 EP - 291 PG - 7 DO - 10.1109/FSKD.2015.7381955 UR - https://m2.mtmt.hu/api/publication/2957978 ID - 2957978 N1 - Budapest University of Technology and Economics, Budapest, Hungary Sapientia University of Transylvania, Tîrgu MureŚ, Romania Conference code: 119123 Cited By :22 Export Date: 10 June 2021 LA - English DB - MTMT ER - TY - JOUR AU - Szilágyi, László AU - Szilágyi, Sándor Miklós TI - Generalization rules for the suppressed fuzzy c-means clustering algorithm JF - NEUROCOMPUTING J2 - NEUROCOMPUTING VL - 139 PY - 2014 SP - 298 EP - 309 PG - 12 SN - 0925-2312 DO - 10.1016/j.neucom.2014.02.027 UR - https://m2.mtmt.hu/api/publication/2698866 ID - 2698866 N1 - Sapientia University of Transylvania, Faculty of Technical and Human Sciences, 540485 Tîrgu Mureş, Şoseaua Sighişoarei 1/C, Romania Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, H-1117 Budapest, Magyar tudósok krt. 2, Hungary Petru Maior University, Department of Informatics, 540088 Tîrgu Mureş, Str. Nicolae Iorga Nr. 1, Romania Export Date: 13 March 2025; Cited By: 31; Correspondence Address: S.M. Szilágyi; Petru Maior University, Department of Informatics, 540088 Tîrgu Mureş, Str. Nicolae Iorga Nr. 1, Romania; email: szsandor72@yahoo.com; CODEN: NRCGE AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Szilágyi, László TI - Lessons to learn from a mistaken optimization JF - PATTERN RECOGNITION LETTERS J2 - PATTERN RECOGN LETT VL - 36 PY - 2014 IS - 1 SP - 29 EP - 35 PG - 7 SN - 0167-8655 DO - 10.1016/j.patrec.2013.08.027 UR - https://m2.mtmt.hu/api/publication/2695297 ID - 2695297 N1 - Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, H-1117 Budapest, Magyar tudósok krt. 2, Hungary Sapientia University of Transylvania, Faculty of Technical and Human Sciences, 540485 Tirgu Mures, Şoseaua Sighişoarei 1/C, Romania Export Date: 13 March 2025; Cited By: 22; Correspondence Address: L. Szilágyi; Sapientia University of Transylvania, Faculty of Technical and Human Sciences, 540485 Tirgu Mures, Şoseaua Sighişoarei 1/C, Romania; email: lalo@ms.sapientia.ro AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Szilágyi, László TI - Robust spherical shell clustering using fuzzy-possibilistic product partition JF - INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS J2 - INT J INTELL SYST VL - 28 PY - 2013 IS - 6 SP - 524 EP - 539 PG - 16 SN - 0884-8173 DO - 10.1002/int.21591 UR - https://m2.mtmt.hu/api/publication/2694345 ID - 2694345 N1 - Export Date: 13 March 2025; Cited By: 7; Correspondence Address: L. Szilágyi; Sapientia-Hungarian University of Transylvania, Faculty of Technical and Human Sciences Romania, 540485 Tîrgu-Mureş, Romania; email: lalo@ms.sapientia.ro; CODEN: IJISE AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Szilágyi, László ED - Torra, V ED - Narakawa, Y ED - Yin, J ED - Long, J TI - Fuzzy-possibilistic product partition: A novel robust approach to c-means clustering T2 - Modeling Decisions for Artificial Intelligence PB - Springer Netherlands CY - Heidelberg CY - Berlin SN - 9783642225888 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 6820. PY - 2011 SP - 150 EP - 161 PG - 12 DO - 10.1007/978-3-642-22589-5_15 UR - https://m2.mtmt.hu/api/publication/3344749 ID - 3344749 N1 - Export Date: 13 March 2025; Cited By: 26; Correspondence Address: L. Szilágyi; Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania; email: lalo@ms.sapientia.ro; Conference name: 8th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2011; Conference date: 28 July 2011 through 30 July 2011; Conference code: 85898 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Szilágyi, László AU - Benyó, Zoltán AU - Szilágyi, Sándor Miklós AU - Adam, Hatem Salem ED - Leder, RS TI - MR Brain Image Segmentation Using an Enhanced Fuzzy C-Means Algorithm T2 - PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9780780377899 T3 - Proceedings - Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), ISSN 1094-687X PY - 2003 SP - 724 EP - 726 PG - 3 DO - 10.1109/IEMBS.2003.1279866 UR - https://m2.mtmt.hu/api/publication/2618867 ID - 2618867 LA - English DB - MTMT ER -