In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved w...

This paper illustrates an algorithm for osteosarcoma segmentation, using vectorial fuzzy-connectedness segmentation, and coming up with a methodology which can be used to segment some distinct tissues of osteosarcoma such as tumor, necrosis and parosteal sarcoma from 3D vectorial images. However, fuzzy-connectedness segmentation can be successfully used only in connected regions. In this paper,...

The generic fuzzy rule-based image segmentation technique (GFRIS) does not produce good results for non-homogeneous reg’ons that possess abrupt changes in pixel intensity, because if fails to consider two important properties of perceptual grouping, namely surroundedness and connectedness. In this paper a new technique called exfended fuzzy rules for image segmentation (EFRIS) isproposed, which...

In this paper we study the segmentation of coronary arteries from bidimensional echocardiography simulated images (phantoms) using Fuzzy Connectedness concepts implemented using Image Foresting Transform (IFT). This approach transforms the image into an oriented and weighted graph; therewith a graph-based algorithm can be applied to process the image segmentation. In echocardiographic scenario,...

Molodtsov first proposed the soft set theory, which can be used as a general mathematical tool for dealing with fuzzy and uncertain information. As a generalization of soft set, interval-valued intuitionistic fuzzy soft set is another soft set structure,it is a combination of an interval-valued intuitionistic fuzzy set and a soft set. The objective of this paper is to study the decision making ...

For any positive integer M, M-object fuzzy connectedness (FC) segmentation is a methodology for finding M objects in a digital image based on user-specified seed points and user-specified functions, called (fuzzy) affinities, which map each pair of image points to a value in the real interval [0,1]. FC segmentation has been used with considerable success on biomedical and other images. We provi...

Segmentation is an important step for the diagnosis of multiple sclerosis. In this paper, a method for segmentation of multiple sclerosis lesions from Magnetic Resonance (MR) brain image is proposed. The proposed method combines the strengths of two existing techniques: fuzzy connectedness and artificial neural networks. From the input MR brain image, the fuzzy connectedness algorithm is used t...

The generic fuzzy rule-based image segmentation technique (GFRIS) does not produce good results for non-homogeneous reg’ons that possess abrupt changes in pixel intensity, because if fails to consider two important properties of perceptual grouping, namely surroundedness and connectedness. In this paper a new technique called exfended fuzzy rules for image segmentation (EFRIS) isproposed, which...