background and objectives: accurate detection of type and severity of hepatitis is crucial for effective treatment of the disease. while several computational algorithms for detection of hepatitis have been proposed to date, their limited performance leaves room for further improvement. this paper proposes a novel computational method for the diagnosis of hepatitis b using pattern detection tec...

Brain tissue segmentation of Magnetic Resonance Imaging (MRI) is an important and one of the challenging tasks in medical image processing. MRI images of brain are classified into two types: classifying tissues, anatomical structures. It comprised into different tissue classes which contain four major regions, namely Gray matter (GM), White matter (WM), Cerebrospinal fluid (CSF), and Background...

Functional magnetic resonance imaging (FMRI) datasets are composed of spatial and temporal features and contain unique noise degradation. We propose a feature partition along noise-specific domains to fit the fuzzy c-means (FCM) algorithm to this problem. Each domain will consist of unique features and use a domainspecific metric. The distance term in the FCM membership update equation is repla...

Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy c-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concep...

Clustering is a popular unsupervised pattern recognition technique to partition the set of similar or dissimilar object groups. Fuzzy c-means (FCM) is the current hot topic of interest among the data mining and other research communities. But sometimes, it gets trapped at local minima positions and for which it is unable to produce effective results. Inspired by this fact, in this paper an atte...

A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

The study of alveolar macrophages (AM) from smokers by flow cytometry (FCM) has been limited by strong autofluorescence and the lack of reliable markers to identify macrophages. Crystal violet quenching was reported to be effective in reducing autofluorescence of AM. CD68 is a marker for macrophages in immunohistochemistry, but has been less useful in FCM because of poor surface expression. Thi...

In brain magnetic resonance (MR) images, image segmentation and 3D visualization are very useful tools for the diagnosis of abnormalities. Segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is the basic process for 3D visualization of brain MR images. Of the many algorithms, the fuzzy c-means (FCM) technique has been widely used for segmentation of brain MR image...

As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM a...