International Journal of Computational Intelligence and Applications

In this paper, the texture property "coarseness" is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated with a measure value. In our study, a wide variety of measures is analyzed, and assessments about coarseness perception are collected from pools. This information is used to obtain type-2 fuzzy sets where the secondary fuzzy sets are modeled by means of triangular membership functions fitted to the collected data.

The Pap Smear test, or cervico-vaginal cytology, is globally the most used and suitable method for screening cervical cancer precursor lesions, with a significant impact in reducing the incidence and mortality rates. However, Pap test suffers from subjective variability and no specificity, being the most controversial point the persistence of false negatives; i.e., normal cytological report for a woman with existing dysplasia, pre-malignant, or malignant lesions of the cervix. This is due in large part to the vast number of cells that must be reviewed by a technician for determining the possible existence of a small number of malignant or pre-malignant cells.

Automated systems that include technician knowledge and interpretation could not only reduce sample examination time but also avoid misclassification of samples because of human errors.

Here we present part of our ongoing work toward automation of cervical screening process. Specifically, since in cytological studies nuclei are considered the most informative regions, and an accurate segmentation is needed for extracting meaningful cell features, we propose an automated nuclei detection algorithm that integrates color information, cytopathologists knowledge, and fuzzy systems. Results have shown that besides a high performance and efficiency, the speed of the algorithm is very high.

We propose a novel approach to 3D image registration of intracellular volumes. The approach extends a standard image registration framework to the curved cell geometry. An intracellular volume is mapped onto another intracellular domain by using two pairs of point set surfaces approximating their nuclear and plasma membranes. The mapping function consists of the affine transformation, tetrahedral barycentric interpolation, and least-squares formulation of radial basis functions for extracted cell geometry features. An interactive volume registration system is also developed based on our approach. We demonstrate that our approach is capable of creating cell models containing multiple organelles from observed data of living cells.

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.^[14] In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the cluster centers. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.

Important soccer tournaments like the World Cup and the European Cup are broadcast to billions of people across the world. Therefore, advertising through billboards surrounding the playing arena in a soccer match is very attractive for promoting the brand image of a company. Analysis of strength and weakness of such an advertisement is helpful for the sponsors since a lot of money is spent on it and due to its potential impact on a large number of viewers at the same time. In this paper, we present a two-stage fuzzy system for analyzing the visual impact of advertisement billboards in soccer telecasts. In the first stage, visual impact of each shot is evaluated. Two parameters, namely, size and duration, of the billboards are considered in the fuzzy rules. After the shot level analysis, results are combined in the second stage to derive an overall visual impact. In both the stages, parameters of the fuzzy set membership functions are tuned using the Particle Swarm Optimization algorithm. The results have been compared against a user survey.