We propose an innovative segmentation algorithm based on mathematical morphology operators. This definition is based on a morphological and fuzzy pattern-matching approach, and consists in comparing an object to a fuzzy landscape representing the degree of satisfaction of an affinity relationship. It has good formal properties, it is flexible, it fits the intuition, and it can be used for struc...

In this paper, we present an algorithm to infer a fuzzy partition over a set of numerical values. This algorithm is based on mathematical morphology and is expressed in theory of formal language. It is usable when the numerical values are associated with a class. We use this algorithm during the construction of a fuzzy decision tree, when no fuzzy partition is available for a numerical attribute.

One of the powerful features of mathematical morphology lies in its strong algebraic structure, that finds equivalents in set theoretical terms, fuzzy sets theory and logics. Moreover this theory is able to deal with global and structural information since several spatial relationships can be expressed in terms of morphological operations. The aim of this paper is to show that the framework of ...

In this paper we discuss how biresiduations provide a unifying paradigm for fuzzy formal concept analysis and mathematical morphology. In particular we provide constructions of morphological operators such as dilation and erosion within the framework of convolution algebras of action networks (also known as small covariant categories) over join complete semirings. This unifies and generalizes p...

Mathematical Morphology is a theory concerned with the processing and analysis of images or signals using filters and other operators that modify them. This paper studies how the original images and signals can be retrieved using fuzzy property-oriented concept lattices and fuzzy relation equations.

In image analysis and pattern recognition fuzzy sets play the role of a good model for segmentation and classifications tasks when the regions and the classes cannot be strictly defined. One of the most widely used fuzzy approaches in image processing is fuzzy mathematical morphology because of the underlaying structure of complete lattices. Thus, mathematical morphology is used mainly in proce...

The appropriate mathematical framework of mathematical morphology (MM) is given by complete lattices. Recently, the observation that the class of intervalvalued fuzzy sets constitutes a complete lattice has given rise to an extension of fuzzy MM (FMM) called interval-valued fuzzy mathematical morphology. In contrast to FMM, interval-valued FMM allows us to model uncertainty in the pixel values ...

The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retin...

Active Learning Method (ALM) is a soft computing method used for modeling and control based on fuzzy logic. All operators defined for fuzzy sets must serve as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling method, ALM does not possess operators which serve as S-norms and T-norms which deprive it of a profound analytical expression/form. This paper introduces two new oper...

Background Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. Materials and Methods We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morp...