George D Magoulas | Search PubMed

PubMed2006

Neuronal networks and textural descriptors for automated tissue classification in endoscopy.

This study examines the potential of neuronal networks and textural feature extraction for recognising suspicious regions in endoscopy under variable perceptual conditions and systematic or random noise in the data. Second-order statistics and discrete wavelet transform-based methodologies are examined in terms of their discrimination abilities, and several neuronal network learning algorithms are compared in terms of success. The results provide numerical evidence that neuronal networks are capable of classifying offline and online tissue samples extracted from standard images and VHS videotape recordings of colonoscopy procedures with satisfactory success rates. This type of technology could prove to be useful for developing intelligent adaptive systems that will assist medical experts in real-time to automate minimally invasive diagnostic procedures.

Artifacts↗

PubMed2003

Improved processing of microarray data using image reconstruction techniques.

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy.

Algorithms↗