Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

Process of construction of an educational booklet for health promotion of pregnant women

This study aimed to describe the production process of an educational booklet focusing on health promotion of pregnant women. The action research method was used in this process composed of the following steps: choice of the content based on the needs of pregnant women, creation of illustrations, content preparation based on scientific literature, validation of the material by experts and pregnant women. This work resulted in the final version of the booklet, which was entitled "Celebrating life: our commitment with the health promotion of pregnant women". Active participation of health professionals and pregnant women through dialogue and collective strategy permeated the process of development of the booklet. The opinions of pregnant women and experts who considered the booklet enriching and enlightening justify the use of it as an additional resource of educational activities carried out during the prenatal care.

Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking

This paper presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the image foresting transform with a never-exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods such as live wire and graph cuts. Several experiments show that riverbed can significantly reduce the number of user interactions (anchor points), as compared to live wire for objects with complex shapes. This paper also includes a discussion about how to combine different methods in order to take advantage of their complementary strengths.

A simple boiling-based DNA extraction for RAPD profiling of landfarm soil to provide representative metagenomic content

Landfarm soils are employed in industrial and petrochemical residue bioremediation. This process induces selective pressure directed towards microorganisms capable of degrading toxic compounds. Detailed description of taxa in these environments is difficult due to a lack of knowledge of culture conditions required for unknown microorganisms. A metagenomic approach permits identification of organisms without the need for culture. However, a DNA extraction step is first required, which can bias taxonomic representativeness and interfere with cloning steps by extracting interference substances. We developed a simplified DNA extraction procedure coupled with metagenomic DNA amplification in an effort to overcome these limitations. The amplified sequences were used to generate a metagenomic data set and the taxonomic and functional representativeness were evaluated in comparison with a data set built with DNA extracted by conventional methods. The simplified and optimized method of RAPD to access metagenomic information provides better representativeness of the taxonomical and metabolic aspects of the environmental samples.