Search results for "Fuzzy cluster"
showing 10 items of 32 documents
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
2022
Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…
Accurate detection and characterization of corner points using circular statistics and fuzzy clustering
1998
Accurate detection and characterization of corner points in grey level images is considered as a pattern recognition problem. The method considers circular statistic tests to detect 2D features. A fuzzy clustering algorithm is applied to the edge orientations near the prospective corners to detect and classify them. The method is based on formulating hypotheses about the distribution of these orientations around an edge, corner or other 2-D feature. The method may provide accurate estimates of the direction of the edges that converge in a corner, along with their confidence intervals. Experimental results show the method to be robust enough against noise and contrast changes. Fuzzy membersh…
A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm
2015
The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-dow…
Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic
2016
Distributed denial-of-service (DDoS) attacks are one of the most serious threats to today’s high-speed networks. These attacks can quickly incapacitate a targeted business, costing victims millions of dollars in lost revenue and productivity. In this paper, we present a novel method which allows us to timely detect application-layer DDoS attacks that utilize encrypted protocols by applying an anomaly-based approach to statistics extracted from network packets. The method involves construction of a model of normal user behavior with the help of weighted fuzzy clustering. The construction algorithm is self-adaptive and allows one to update the model every time when a new portion of network tr…
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
2017
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…
A Fuzzy-Clustering Based Approach for Measuring Similarity Between Melodies
2017
Symbolic melodic similarity aims to evaluate the degree of likeness of two or more sequences of notes. In this work, we propose the use of fuzzy c-means clustering as a tool for the measurement of the similarity between two melodies with a different number of notes. Moreover, we present an algorithm, FOCM, implemented in a computer program written in C\(\sharp \) able to read two melodies from files with MusicXML format and to perform the clustering to calculate the dissimilarity between any two melodies. In addition, for each iteration step in the convergence process of the algorithm, a family of intermediate states (transition melodies) are obtained that can be used as new thematic materi…
A 3-D marker-free system for the analysis of movement disabilities--an application to the legs.
2001
The aim of this paper is to describe an approach allowing the analysis of human motion in three-dimensional (3-D) space. The system that we developed is composed of three charge-coupled-device cameras that capture synchronized image sequences of a human body in motion without the use of markers. Characteristic points belonging to the boundaries of the body in motion are first extracted from the initial images. Two-dimensional superquadrics are then adjusted on these points by a fuzzy clustering process. After that, the position of a 3-D model based on a set of articulated superquadrics, each of them describing a part of the human body, is reconstructed. An optical flow process allows the pr…
Mercury$$^\mathrm{\textregistered }$$: A Software Based on Fuzzy Clustering for Computer-Assisted Composition
2019
We present Mercury, a new software for computer-assisted composition based on fuzzy clustering algorithms. This software is able to generate a big number of transitions between any two different melodies, harmonic progressions or rhythmical patterns. Mercury works with symbolic music notation. The software is, therefore, able to read music and to export the generated musical production into MusicXML format. This paper focusses on some theoretical aspects of the CFT algorithm implemented in the software in order to create those complete transitions, overviewing not only the structure of the program but the user’s interface and its music notation module. Finally, the wide variety of compositi…
Fuzzy Smoothed Composition of Local Mapping Transformations for Non-rigid Image Registration
2009
This paper presents a novel method for medical image regis- tration. The global transformation is obtained by composing affine trans- formations, which are recovered locally from given landmarks. Transfor- mations of adjacent regions are smoothed to avoid blocking artifacts, so that a unique continuous and differentiable global function is obtained. Such composition is operated using a technique derived from fuzzy C- means clustering. The method was successfully tested on several datasets; results, both qualitative and quantitative, are shown. Comparisons with other methods are reported. Final considerations on the efficiency of the technique are explained.
Cluster classification of dysphagia-oriented products considering flow, thixotropy and oscillatory testing
2011
Abstract The present paper proposes an objective classification of commercial thickened food widely used in patients with dysphagia. A total of 34 commercial enteral nutrition products were analyzed (beverages, main courses and desserts) corresponding to 6 different commercial brands. All these products contain different hydrocolloids (i.e., starch, xanthan gum, carrageenans, etc.) as thickeners, in order to get the desired texture. Joint consideration has been made of viscous behavior (flow and thixotropy) and viscoelastic behavior (oscillatory testing). Rheological measurements were fitted to different rheological and empirical mathematical models, and a total of 11 parameters were genera…