Biomedical subjects

António J R Neves

Publications and source records attributed to António J R Neves.

3 recordsLinked to original sources

PubMed2006

On the use of standards for microarray lossless image compression.

The interest in methods that are able to efficiently compress microarray images is relatively new. This is not surprising, since the appearance and fast growth of the technology responsible for producing these images is also quite recent. In this paper, we present a set of compression results obtained with 49 publicly available images, using three image coding standards: lossless JPEG2000, JBIG, and JPEG-LS. We concluded that the compression technology behind JBIG seems to be the one that offers the best combination of compression efficiency and flexibility for microarray image compression.

Data Compression↗

PubMed2006

A three-state model for DNA protein-coding regions.

It is known that the protein-coding regions of DNA are usually characterized by a three-base periodicity. In this paper, we exploit this property, studying a DNA model based on three deterministic states, where each state implements a finite-context model. The experimental results obtained confirm the appropriateness of the proposed approach, showing compression gains in relation to the single finite-context model counterpart. Additionally, and potentially more interesting than the compression gain on its own, is the observation that the entropy associated to each of the three base positions of a codon differs and that this variation is not the same among the organisms analyzed.

Algorithms↗

PubMed2004

A survey on palette reordering methods for improving the compression of color-indexed images.

Palette reordering is a well-known and very effective approach for improving the compression of color-indexed images. In this paper, we provide a survey of palette reordering methods, and we give experimental results comparing the ability of seven of them in improving the compression efficiency of JPEG-LS and lossless JPEG 2000. We concluded that the pairwise merging heuristic proposed by Memon et al. is the most effective, but also the most computationally demanding. Moreover, we found that the second most effective method is a modified version of Zeng's reordering technique, which was 3%-5% worse than pairwise merging, but much faster.

Algorithms↗