We evaluated the validity and accuracy of cytomorphology and multiparametric flow cytometry (C-FCM) in diagnosing oncohematologic disease in 223 consecutive lymph node biopsy specimens from patients with lymphadenopathy, from 2004 to 2007. C-FCM and histopathologic studies were interpreted independently by hematologists and pathologists, respectively. C-FCM detected neoplastic disorders in 133 ...

We investigated the accuracy and precision of flow cytometric (FCM) estimates of bacterial abundances using 4', 6-diamidino-2-phenylindole (DAPI) and Hoechst 33342 (HO342, a bisbenzamide derivative) on paraformaldehyde-fixed seawater samples collected from two stations near Oahu, Hawaii. The accuracy of FCM estimates was assessed against direct counts by using epifluorescence microscopy. DAPI a...

Sample preparation protocols for flow cytometry (FCM) analysis with five algal viruses were optimized: Heterocapsa circularisquama virus (HcV), Heterosigma akashiwo virus (HaV), Chaetoceros salsugineum nuclear inclusion virus (CsNIV), Rhizosolenia setigera RNA virus (RsRNAV) and H. circularisquama RNA virus (HcRNAV). The optimum staining protocols differed significantly among the viruses tested...

Researchers have observed that multistage clustering can accelerate convergence and improve clustering quality. Two-stage and two-phase fuzzy C-means (FCM) algorithms have been reported. In this paper, we demonstrate that the FCM clustering algorithm can be improved by the use of static and dynamic single-pass incremental FCM procedures. Keywords-Clustering; Fuzzy C-Means Clustering; Incrementa...

Seismic hazard assessment like many other issues in seismology is a complicated problem, which is due to a variety of parameters affecting the occurrence of an earthquake. Uncertainty, which is a result of vagueness and incompleteness of the data, should be considered in a rational way. Using fuzzy method makes it possible to allow for uncertainties to be considered. Fuzzy inference system,...

In machine learning the Fuzzy c-Means algorithm (FCM) plays an important role. This prototype based unsupervised clustering method has been extensively studied and applied to a great variety of problems from different research areas like medicine and biology. Commonly the Euclidean distance is used as dissimilarity measure, although any dissimilarity measure would be suited. Recently divergence...

Fuzzy Cognitive Map (FCM) is a soft computing modelling methodology for complex systems. Beyond the mathematical formulation of the FCM theory, there was a need of developing a software tool to facilitate the implementation of FCMs. This paper describes the use of a software tool that was developed to construct FCM models. Some theoretical elements of Fuzzy Cognitive Maps are presented. Then, i...

In the computer aided medical image process, image segmentation is always required as a preprocess stage. Fuzzy c-means (FCM) clustering algorithm has been commonly used in many medical image segmentations, particularly in the analysis of magnetic resonance (MR) brain image. However, all of these FCM methods are computation consuming that is difficult to be used in real time application. In the...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

Stress responses play a critical role in the ecology and demography of wild animals, and the analysis of fecal hormone metabolites is a powerful noninvasive method to assess the role of stress. We characterized the metabolites of injected radiolabeled cortisol in the urine and feces of Columbian ground squirrels and validated an enzyme immunoassay for measuring fecal cortisol metabolites (FCM) ...