Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

Morphological and genetic variation in the north Atlantic copepod, Centropages typicus

This study describes phenotypic and genotypic variations in the planktonic copepod, Centropages typicus (Copepoda: Calanoida) that indicate differentiation between geographical samples. We found consistent differences in the morphology of the chela of the sexually modified fifth pereiopod (P5) of male C. typicus between samples from the Mediterranean, western North Atlantic and eastern North Atlantic. A 560 base pairs (bp) region of the C. typicus mitochondrial cytochrome c oxidase subunit I (COI) and a 462 bp fragment of the nuclear rDNA internal transcribed spacer (ITS) tandem array were analysed to determine whether these morphological variations reflect population genetic differentiation. Mitochondrial haplotype diversity was found to be high with 100 unique COI haplotypes among 116 individuals. Analysis of mtCOI variation suggested differentiation between the Mediterranean and Atlantic populations but no separation was detected within the Atlantic. Intragenomic variation in the ITS array suggested genetic differentiation between samples from the western North Atlantic and those from the eastern North Atlantic and Mediterranean. Breeding experiments would be required to elucidate the extent of genetic isolation between C. typicus from the different population centres.

A study of the morphological variability in Cynara humilis L. and C. Hystrix BalI (Aseraceae-Cardueae)

El estudio de los especimenes de herbario, recolectados en la Península Ibérica y Marruecos, y que han sido atribuidos a Cvttara humilis L y C hystrtc Raíl ha puesto de manifiesto la variabilidad de ciertos caracteres, mucho más notable en eí material marroquí. La representación mediante símbolos, en un mapa, de la distribución de seis dc los caracteres morfológicos seleccionados, permite comprobar la existencia de formas intermedias, que se situan en la zona de contacto entre las áreas marroquíes dc ambos túxones. Sc discute la posibilidad de explicar este hecho, como resultado de un proceso de hibridación alopátrica introgresiva.

GeneGrid: Architecture, Implementation and Application

The emergence of Grid computing technology has opened up an unprecedented opportunity for biologists to share and access data, resources and tools in an integrated environment leading to a greater chance of knowledge discovery. GeneGrid is a Grid computing framework that seamlessly integrates a myriad of heterogeneous resources spanning multiple administrative domains and locations. It provides scientists an integrated environment for the streamlined access of a number of bioinformatics programs and databases through a simple and intuitive interface. It acts as a virtual bioinformatics laboratory by allowing scientists to create, execute and manage workflows that represent bioinformatics experiments. A number of cooperating Grid services interact in an orchestrated manner to provide this functionality. This paper gives insight into the details of the architecture, components and implementation of GeneGrid.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.