Abstract
The analysis of dot blot (macroarray) images is currently based on the human identification of positive/negative dots, which is a subjective and time consuming process. This paper presents a system for the automatic analysis of dot blot images, using a pre-defined grid of markers, including a number of ON and OFF controls. The geometric deformations of the input image are corrected, and the individual markers detected, both tasks fully automatically. Based on a previous training stage, the probability for each marker to be ON is established. This information is provided together with quality parameters for training, noise and classification, allowing for a fully automatic evaluation of a dot blot image.

Abstract
Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The effective implementation of such a tool could lead to a reduction in the number of cases selected for exeresis, with obvious benefits both to the patients and to the health care system. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) image segmentation, (ii) feature extraction and feature selection, (iii) lesion classification. This paper presents a comparison of segmentation methods applied to 50 dermoscopic image analysis, along with a clinical evaluation of each segmentation result performed by an experienced dermatologist.

Abstract
Labeled molecular markers are an important tool in molecular biology. This work presents a method for the automatic identification of molecular markers in dot blot images. The method detects the location of markers in the image and their size. An experiment was made with 6 test images, which were used to produce an additional set of 222 images with various rotation, translation, contrast and noise levels. Over 7500 markers were identified automatically and compared with reference values obtained manually. The RMS error for the marker positioning in the original test images were between 1.1 and 3.8 pixels, which is about 1/10 of the typical radius (26 pixels). The method proposed was found to be almost insensitive to grid rotation and translation, and reasonably robust to image contrast changes and presence of noise.

Abstract
DNA-based methods for bacterial detection are increasingly acknowledged as specific, reliable and cost-effective alternatives or complements to the classical culture-based methods, facilitating early detection and presumptive diagnosis, in particular when symptomless plants and quarantine procedures are involved. The massive number of non-redundant bacterial DNA sequences available in gene banks nowadays, and the accessibility to resourceful bioinformatics tools to analyse those sequences, provide an excellent and extensive source for new genus-, species-, or biovar-specific molecular markers. In this study, we propose an in silico workflow framed by four successive criteria and using two web-based bioinformatics applications, CUPID and Insignia, to identify specific chromosomal loci for phytopathogenic bacteria belonging to the genus Agrobacterium (herein called 'agrobacteria'), useful as DNAmarkers to detect pathogenic strains (belonging to several species) of the genus. The selection criteria for the markers are: (i) retrieve the sequence overlaps between CUPID and Insignia databases using a C++ program developed by us in order to increase marker consistency; (ii) the chosen markers should undergo a BLAST (basic local alignment search tool) analysis to confirm their specificity; (iii) an evolutionary and comparative genomic analysis should allow to infer the likelihood of the putative markers being exclusive for the target taxon, and simultaneously ubiquitous within the taxon diversity; (iv) markers should be chosen regarding their suitability for a particular detection technique, e.g. PCR or hybridization profiling.

Abstract
Dermoscopy is a non-invasive diagnosis technique for in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the development of computer assisted diagnosis systems, given their great potential to this area of medicine. The standard approach in automatic dermoscopic image analysis can be divided in three stages: image segmentation, feature extraction/selection and lesion classification. In order to validate the algorithms developed for each stage, a great number of reliable images and clinical diagnosis are required. This paper presents a software tool to collect and organize dermoscopic data from hospital databases. It is suitable for clinical daily routine and simultaneously has a datastructure to support the development and validation of algorithms created by the researchers to construct the computer assisted diagnosis system. This tool is composed by a database with three related but independent modules: Clinical Module, Processing Module and Statistical Module.

Abstract
Phytosanitary regulations and the provision of plant health certificates still rely mainly on long and laborious culture-based methods of diagnosis, which are frequently inconclusive. DNA-based methods of detection can circumvent many of the limitations of currently used screening methods, allowing a fast and accurate monitoring of samples. The genus Xanthomonas includes 13 phytopathogenic quarantine organisms for which improved methods of diagnosis are needed. In this work, we propose 21 new Xanthomonas-specific molecular markers, within loci coding for Xanthomonas-specific protein domains, useful for DNA-based methods of identification of xanthomonads. The specificity of these markers was assessed by a dot blot hybridization array using 23 non-Xanthomonas species, mostly soil dwelling and/or phytopathogens for the same host plants. In addition, the validation of these markers on 15 Xanthomonas spp. suggested species-specific hybridization patterns, which allowed discrimination among the different Xanthomonas species. Having in mind that DNA-based methods of diagnosis are particularly hampered for unsequenced species, namely, Xanthomonas fragariae, Xanthomonas axonopodis pv. phaseoli, and Xanthomonas fuscans subsp. fuscans, for which comparative genomics tools to search for DNA signatures are not yet applicable, emphasis was given to the selection of informative markers able to identify X. fragariae, X. axonopodis pv. phaseoli, and X. fuscans subsp. fuscans strains. In order to avoid inconsistencies due to operator-dependent interpretation of dot blot data, an image-processing algorithm was developed to analyze automatically the dot blot patterns. Ultimately, the proposed markers and the dot blot platform, coupled with automatic data analyses, have the potential to foster a thorough monitoring of phytopathogenic xanthomonads.