Belén Prados-Suárez

Region-based fit of color homogeneity measures for fuzzy image segmentation

In this paper we introduce an approach to automatically select a homogeneity measure for color image segmentation, on the basis of the characteristics of the region to be segmented. In a previous work we presented a fuzzy color path-based image segmentation proposal where membership degrees were computed from the connectivity between pixels, based on the homogeneity degree of the path joining them. To measure homogeneity, we aggregate resemblances between consecutive pixels using t-norms. Since a great variety of homogeneity measures can be found, we need to automatically select a suitable t-norm for a given region. For this purpose we firstly approximate a value characterizing the region surrounding the seed, studying a set of fixed paths. Secondly, we establish a functional relationship between this value and the parameter of a Weber t-norm. Based on this functional relationship we obtain the value of t-norms parameter, corresponding to the homogeneity measure to be used in the segmentation process. We show that our approach performs well in different types of regions.

A hierarchical approach to fuzzy segmentation of colour images

In this paper we introduce a methodology for the segmentation of colour images by means of a nested hierarchy of fuzzy partitions. Colour image segmentation attempts to divide the pixels of an image in several homogeneously-coloured and topologically connected groups, called regions. Our methodology deals with the different (but related) aspects of imprecision that are present in this process. First, the concept of homogeneity in a colour space is imprecise, so a measure of distance/similarity between colours is introduced. As a direct consequence, boundaries between regions are imprecise in general, so it is convenient to define regions as fuzzy subsets of items. The proposed distance in a perceptual colour space is employed to calculate fuzzy regions and membership degrees. In addition, fuzzy segmentation can be different depending on the precision level we consider when looking for homogeneity. Starting from an initial fuzzy segmentation, a hierarchical approach, based on a similarity relation between regions, is employed to obtain a nested hierarchy of regions at different precision levels.

Improving electronic health records retrieval using contexts

Highlights? We present the Electronical Health Records system of an hospital and its problems. ? We improve the access to EHR defining contexts and pertinent data groups. ? We propose an adaptable model to access the pertinent data groups to each access. ? We present the implementation and the evaluation of the proposed system. This paper aims to solve a recently arose problem, related to the access to the Electronic Health Records (EHR) in the Hospitals. Due to the digitalization of the information contained in the medical records, and the growing availability of devices that directly generate digital documents to include in it, the EHR are becoming unmanageable. Even more, to find a concrete item of information relevant for a given assistance act is a very hard, difficult and time-consuming task. To solve it we propose here the definition of contexts of access to the EHR, to exploit the logical division of the information inside each document in the EHR into data groups, and the computation of the pertinence of each data group to each context. It allows us to prioritize, the information in the EHR, even at a concrete data item level, according to the situation from which it is acceded. This way when the medical personnel is involved in an assistance act, the most relevant information for it is the one first showed, being able to widen the search but always according to the relevance. With it we not only improve the accessibility to the EHR and make easier the work of the doctors, but also enable other applications like the ubiquitous computation or the mobility, using devices like tablet PCs and PDAs.

Using Fuzzy Sets for Coarseness Representation in Texture Images

Texture is a visual feature frequently used in image analysis that has associated certain vagueness. However, the majority of the approaches found in the literature do not either consider such vagueness or they do not take into account human perception to model the related uncertainty. In this paper we model the concept of ”coarseness”, one of the most important textural features, by means of fuzzy sets and considering the way humans perceive this kind of texture. Specifically, we relate representative measures of coarseness with its presence degree. To obtain these ”presence degrees”, we collect assessments from polls filled by human subjects, performing an aggregation of such assessments. Thus, the membership function corresponding to the fuzzy set ”coarseness” is modelled by using as reference set the representative measures and the aggregated data.

A Fuzzy Colour Image Segmentation Applied to Robot Vision

In this paper, a new growing region algorithm to segment colour images is proposed. A region is defined as a fuzzy subset of connected pixels and it is constructed using topographic and colour information. To guide the growing region process, a distance defined in the HSI colour space is proposed. This distance is used both to select the pixels which will be linked in each step of the algorithm, and to calculate membership degree of each point to each region. The proposed technique is applied to vision-guided robot navigation with the aim of detecting doors in indoor environments.

Fuzzy Homogeneity Measures for Path-Based Colour Image Segmentation

In this paper we study different measures of path homogeneity for fuzzy path-based image segmentation. We provide fuzzy semantics for the concept of homogeneity in two steps: first, we introduce a fuzzy interpretation of resemblance between feature vectors characterizing neighbor pixels; then, we obtain the homogeneity of a path by aggregating the set of fuzzy resemblances between consecutive pixels in the path. We propose a set of intuitive properties that any suitable aggregation function should verify for this purpose, and we show that these properties are verified by certain families of t-norms. To determine the performance of the proposed functions, a set of experiments is carried out with both synthetic and real images. Finally, the homogeneity functions are used to obtain fuzzy regions in natural images

Contextualized Access to Electronical Health Records in Cardiology

In this paper, we propose a new approach for accessing the electronical health records (EHR), and we apply it to the cardiology medical specialty. Though the use of EHR improves the storage and access to the information in it regarding the previous health records in papers, it entails the risk of having the same problems of huge size and of becoming inoperative and really difficult to handle, especially if the user is looking for a specific data item. Our proposal is based on the contextualization of the access, providing the user with the most important information for the assistance act in which he/she is involved. To do this, we define the set of possible contexts and consider different aspects of the pertinence of the documents to each context. We do it by using fuzzy logic and pay special attention to the efficiency, due to the huge size of the involved databases. Our proposal does not limit the access to the EHR, but establishes a prioritization based on the access needs, which provides the system with an additional advantage, easily enabling the use of new terminals and devices like tablet PCs and PDAs, which have great limitations in the interfaces.

Perception-based fuzzy sets for visual texture modelling

Texture is one of the most used low-level features for image analysis and, in addition, one of the most difficult to characterize. Although there is not an accurate definition for the concept of texture, it is usual for humans to describe visual textures according to some perceptual properties like coarseness, directionality, contrast, line-likeness or regularity. In this paper, we propose to model texture on the basis of its perceptual properties. To do this, fuzzy sets defined on the domain of some of the most representative measures of each property are employed. This approach achieves a double objective: first, to obtain models that allow to represent the imprecision related to texture properties, and second, to identify the most appropriate measure for each of these properties. In order to define the fuzzy models, parametric membership functions are proposed, where the corresponding parameters are obtained by learning a functional relationship between the computational values given by the measure and the human perception of the corresponding property. The performance of each fuzzy set is analyzed and checked with the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure. In order to explain the proposed methodology, we focus our study on coarseness, contrast and directionality, that are considered the three most important texture properties.

An adaptive fuzzy approach for modeling visual texture properties

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. The presence of these properties in images is very difficult to characterize due to their imprecision, and, moreover, because their perception may change depending on the user or the image context. In this paper, texture properties are modeled by means of an adaptive fuzzy approach that takes into account the subjectivity of the human perception. For this purpose, a methodology in two phases has been proposed. First, non-adaptive fuzzy models, that represent the average human perception about the presence of the texture properties, are obtained. For this modeling, we propose to learn a relationship between representative measures of the properties and the assessments given by human subjects. In a second phase, the obtained fuzzy sets are adapted in order to model the particular perception of the properties that a user may have, as well as the changes in perception influenced by the image context. For this purpose, the membership functions are automatically transformed on the basic of the information given by the user or extracted from the image context, respectively.

Fuzzy sets on 2D spaces for fineness representation

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we propose a methodology to model texture properties by means of fuzzy sets defined on bidimensional spaces. In particular, we have focused our study on the fineness property that is considered as the most important feature for human visual interpretation. In our approach, pairwise combinations of fineness measures are used as a reference set, which allows to improve the ability to capture the presence of this property. To obtain the membership functions, we propose to learn the relationship between the computational values given by the measures and the human perception of fineness. The performance of each fuzzy set is analyzed and tested with the human assessments, allowing us to evaluate the goodness of each model and to identify the most suitable combination of measures for representing the fineness presence. We propose to model the fineness property of texture by means of fuzzy sets defined on the domain of pairwise combinations of fineness measures.The membership functions of the proposed fuzzy sets are obtained by taking into account the human perception of fineness.The bidimensional models proposed in this paper are able to represent the presence degree of fineness, matching what a human would expect.