Comparison between laser scanning tomography and computerised image analysis of the optic disc

Aims - To study the interchangeability of the measurements of the optic disc topography obtained by one computerised image analyser and one confocal laser tomographic scanner. Methods - One eye of 28 patients with glaucoma or glaucoma suspects was studied. All cases had simultaneous stereoscopic disc photographs taken with the fundus camera Topcon TRC-SS and optic disc examination with the Heidelberg retina tomograph (HRT) during the same visit. The optic disc photographs were digitised and analysed with the Topcon ImageNet (TI) system. Three variables of the optic disc topography provided by the TI and the HRT were compared - cup volume (CV), rim area (RA), and cup area to disc area ratio (CA/DA). Results - The mean values of CV and RA provided by the TI (0.52 (SD 0.32) mm and 1.58 (0.39) mm , respectively) were greater (p <0.01) than the mean values of CV and RA determined by the HRT (0.32 (0.25) mm , and 1.33 (0.47) mm , respectively). The mean value of CA/DA provided by the TI (0.42 (0.14)) and the HRT (0.42 (0.18)) was similar (p = 0.93). Correlation coefficients between measurements obtained by the two methods ranged from 0.53 to 0.73. Conclusion - There was a significant discrepancy in the measurements of rim area and cup volume of the optic disc obtained by a computerised image analyser and a laser scanning tomograph.

Detection of changes of the optic disc in glaucomatous eyes:Clinical examination and image analysis with the Topcon Imagenet system

Purpose: This study was designed to evaluate the clinical agreement in the detection of optic disc changes and the ability of computerized image analysis to detect glaucomatous deterioration of the optic disc. Methods: Pairs of stereophotographs of 35 glaucomatous optic discs taken 5 years apart and of 5 glaucomatous discs photographed twice on the same day. Two glaucoma specialists examined the pairs of stereophotographs (35 cases and 5 controls) in a masked manner and judged whether the optic disc showed changes in the optic disc compatible with progression of glaucomatous damage. The stereophotographs of the five optic discs photographed twice on the same day (which by definition did not change) and of five cases judged to have deteriorated by both glaucoma specialists were analyzed by computerized image analysis with the Topcon ImageNet system. Intra- and inter-observer agreement in the detection of optic disc changes (evaluated using kappa statistic), and changes in the rim area to disc area ratio (evaluated using descriptive statistics and paired t-test). Results: Intra-observer agreement had a kappa value of 0.75 for observer 1 and 0.60 for the observer 2. Inter-observer agreement between the glaucoma specialists had a kappa value of 0.60. The image analyzer did not discriminate between controls and cases with clinically apparent glaucomatous change of the optic disc. Conclusion: Clinical agreement in detecting changes in the optic disc was moderate to substantial. Computerized image analysis with the Topcon ImageNet system appeared not to be useful in detecting glaucomatous changes of the optic disc.

Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

Automated image analysis for experimental investigations of salt water intrusion in coastal aquifers

A novel methodology has been developed to quantify important saltwater intrusion parameters in a sandbox style experiment using image analysis. Existing methods found in the literature are based mainly on visual observations, which are subjective, labour intensive and limits the temporal and spatial resolutions that can be analysed. A robust error analysis was undertaken to determine the optimum methodology to convert image light intensity to concentration. Results showed that defining a relationship on a pixel-wise basis provided the most accurate image to concentration conversion and allowed quantification of the width of mixing zone between the saltwater and freshwater. A large image sample rate was used to investigate the transient dynamics of saltwater intrusion, which rendered analysis by visual observation unsuitable. This paper presents the methodologies developed to minimise human input and promote autonomy, provide high resolution image to concentration conversion and allow the quantification of intrusion parameters under transient conditions.

Experimental Saltwater Intrusion in Coastal aquifers Using Automated Image Analysis: Applications to Homogeneous Aquifers.

This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimizing manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar
traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

Is it time for standards and a common data exchange format for digital image analysis?

Digital image analysis is at a crossroads. While the technology has made great strides over the past few decades, there is an urgent need for image analysis to inform the next wave of large scale tissue biomarker discovery studies in cancer. Drawing parallels from the growth of next generation sequencing, this presentation will consider the case for a common language or standard format for storing and communicating digital image analysis data. In this context, image analysis data comprises more than simply an image with markups and attached key-value pair metrics. The desire to objectively benchmark competing platforms or a push for data to be deposited to public repositories much like genomics data may drive the need for a standard that also encompasses granular, cell-by-cell data.

Clustering algorithms and shape factor methods to discriminate among small GTPase phenotypes using DIC image analysis.

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès. En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants. Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.