Roberto Rodríguez

Accessibility metrics of web pages for blind end-users

The Internet offers new possibilities to the access of information, but sometimes the design of web pages obstructs the contents making them inaccessible to everybody, especially for those people with visual disabilities. Accessibility of web pages is an area that is gaining more and more interest. Not only do we have technique recommendations from the World Wide Web Consortium but also legal policies following these recommendations in several countries. In order to measure the fulfilment of these guidelines, different tools have been designed. These tools are useful mainly from the point of view of designers. However, they do not offer a global indicator of accessibility to endusers at the moment of surfing the net. For visually handicapped people, especially blind people, not only is a way necessary to know the degree of accessibility of web pages when being visited (not only the page as a whole, but also the different parts of the page). In the context of the project KAI (Kit for the Accessibility to the Internet), an accessibility measurement module has been developed, able to give a global indicator of accessibility at the moment of surfing the net. Moreover, the degree to which accessibility can be obtained in an independent way for each element belonging to the web page. This paper presents the main ideas behind this module.

A Segmentation Algorithm Based on an Iterative Computation of the Mean Shift Filtering

Image segmentation is accepted to be one of the most important problems in image analysis. The good performance of any recognition system strongly depends on the results provided by the segmentation module. According to many researchers, segmentation finishes when the goal of observer is satisfied. Experience has shown that the most effective methods continue to be the iterative algorithms. However, a problem with these algorithms is the stopping criterion. In this work, we present a strategy for image segmentation through a new algorithm based on recursively applying the mean shift filtering, where entropy is used as a stopping criterion. The main feature of the proposed algorithm is to carry out segmentation in an only step. In other words, with the new algorithm is not necessary to carry out additionally the segmentation step, where in many occasions (mainly in complex applications), it can be computationally expensive. The effectiveness of the proposed algorithm is shown through several experimental results. The obtained results proved that the proposed segmentation algorithm is a straightforward extension of the filtering process. In this paper a comparison between our algorithm and so called EDISON System was carried out.

Blood Vessel Segmentation via Neural Network in Histological Images

In this paper we utilize the Kohonens self-organizing feature map to segment blood vessels from biopsies in tumor tissue. The ability of this kind of neural network to recognize very complex patterns makes it an effective computational tool for the segmentation. We propose a strategy of blood vessels segmentation using a neural network, taking into account the quality of our images and its features: complexity in shape and variability in size. Segmentation results are contingently manually corrected. The proposed segmentation strategy is tested on manual segmentation, where segmentation errors of less than 3.5% are observed. This work is a part of a global image analysis process and these images will be subject to a further morphometrical analysis in order to diagnose and prognosticate automatically malignant tumours.

A Strategy for Atherosclerosis Image Segmentation by Using Robust Markers

The watersheds method is a powerful segmentation tool developed in mathematical morphology, which has the drawback of producing over-segmentation. In this paper, in order to prevent its over-segmentation, we present a strategy to obtain robust markers for image segmentation of atherosclerotic lesions of the thoracic aorta. In such sense, we introduced an algorithm, which was very useful in order to obtain the markers of the atherosclerotic lesions. Images were pre-processed using the Gauss filter and a contrast enhancement. The obtained results by using our strategy were validated calculating the false negatives (FN) and false positives (FP) according to criterion of physicians, where 0% for FN and less than 11% for FP were obtained. Extensive experimentation showed that, using real image data, the proposed strategy was very suitable for our application. These images will be subject to an additional morphometrical analysis in order to study automatically the atherosclerosis and its organic-consequences.

An image segmentation algorithm using iteratively the mean shift

Image segmentation plays an important role in many systems of computer vision. The good performance of recognition algorithms depend on the quality of segmented image. According to the opinion of many authors the segmentation concludes when it satisfies the observers objectives, the more effective methods being the iterative. However, a problem of these algorithms is the stopping criterion. In this work the entropy is used as stopping criterion in the segmentation process by using recursively the mean shift filtering. In such sense a new algorithm is introduced. The good performance of this algorithm is illustrated with extensive experimental results. The obtained results demonstrated that this algorithm is a straightforward extension of the filtering process. In this paper a comparison was carried out between the obtained results with our algorithm and with the EDISON System [16].

A comparison between two robust techniques for segmentation of blood vessels

Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observers goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. In this paper, we have carried out a comparison between two current segmentation techniques, namely the mean shift method, for which we propose a new algorithm, and the so-called spectral method. In this investigation the important information to be extracted from an image is the number of blood vessels (BV) present in the image. The results obtained by both strategies were compared with the results provided by manual segmentation. We have found that using the mean shift segmentation an error less than 20% for false positives (FP) and 0% for false negatives (FN) was observed, while for the spectral method more than 45% for FP and 0% for FN were obtained. We discuss the advantages and disadvantages of both methods.

Mathematical Techniques for Biomedical Image Segmentation

Abstract This article presents some mathematical methods for biomedical image segmentation that these authors have used, adapted, and developed. In particular, some segmentation strategies for blood vessel, atherosclerosis, and intracerebral hemorrhage images constitute one of the principal causes of death in all those countries where the classical epidemics do not have an important weight. In the field of biomedical, images have developed sophisticated algorithms for image segmentation, which go from the deformable models, bioinspired algorithms, and neural networks, among others. Many of these strategies for arriving at satisfactory results need a lot of computational time. For this reason, the proposal of simple, fast, and reliable algorithms for biomedical image segmentation will be always welcome. The remainder of the article is presented in section “ Materials and Methods .” Section “ Mathematical Techniques Theory ” outlines some mathematical techniques and theoretical aspects. In section “ Algorithms ,” we describe some of our algorithms. Section “ Segmentation of Blood Vessel Images ” demonstrates the experimental results, comparisons, and discussion. Finally, in section “ Conclusions ,” the most important conclusions are given.

A strategy for atherosclerotic lesions segmentation

The watersheds method is a powerful segmentation tool developed in mathematical morphology, which has the drawback of producing over-segmentation. In this paper, in order to prevent its over-segmentation, we present a strategy to obtain robust markers for atherosclerotic lesions segmentation of the thoracic aorta. In such sense, we introduced an algorithm, which was very useful in order to obtain the markers of atherosclerotic lesions. The obtained results by using our strategy were validated calculating the false negatives (FN) and false positives (FP) according to criterion of pathologists, where 0% for FN and less than 11% for FP were obtained. Extensive experimentation showed that, using real image data, the proposed strategy was very suitable for our application.

Robust Markers for Blood Vessels Segmentation: A New Algorithm

In this paper, we present a new algorithm to obtain robust markers for blood vessels segmentation in malignant tumors. We propose a two-stage segmentation strategy which involves: 1) extracting an approximate region containing blood vessels and part of the background, and 2) segmenting blood vessels from the background within this region. The approach was effectively very useful in blood vessels segmentation and its validity was tested by using the watershed method. The proposed segmentation technique is tested on manual segmentation. It is demonstrated by extensive experimentation, by using real images, that the proposed strategy was suitable for our application.