BackgroundDrusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD). They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually.MethodsThis article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI) by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area.Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated.ResultsThe ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G); each expert compared to the other experts (E2E) and a standard Global Threshold method compared to the ground truth (T2G).The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively.ConclusionsThe gradings produced by AD3RI obtained an agreement with the ground truth similar to the experts (with a higher reproducibility) and significantly better than the Threshold Method. Despite the higher sensitivity of the Threshold method, explained by its over segmentation bias, it has lower specificity and lower kappa coefficient. Therefore, it can be concluded that AD3RI accurately quantifies drusen, using a reproducible method with benefits for ARMD evaluation and follow-up.
Background/Aims: To monitor possible changes in the cumulated drusen or geographic atrophy area size (CDGAS) of nonexudative age-related macular degeneration (AMD) in patients before and after cataract surgery, using a new tool for computer-aided image quantification. Methods: Randomized, prospective, clinical trial. 54 patients with cataract and nonexudative AMD were randomly assigned into an early surgery group (ES = 28) and a control group (CO = 26) with a 6-month delay of surgery. CDGAS was determined with the MD3RI tool for contour drawing in a central region of digitized fundus photographs, measuring 3,000 µm in diameter. To evaluate CDGAS progression, differences in pixels and square millimeters were calculated by equivalent tests. Results: Forty-nine patients completed the visits over the 12-month period (ES = 27 and CO = 22). Mean pixel values increased from 201.5 (11.33 × 10–3 mm2) to 202.7 (11.39 × 10–3 mm2) in the ES group and from 191.6 (10.77 × 10–3 mm2) to 194.6 (10.94 × 10–3 mm2) in the CO group. Finally, equivalence of CDGAS differences between ES and CO could be demonstrated. No exudative AMD was recorded during the study period. Conclusion: In our cohorts, no significant changes were found in CDGAS 12 months after cataract surgery. The MD3RI software could serve as an efficient, precise and objective tool for AMD quantification and monitoring in future trials.
Motivation: Cell division in Escherichia coli is morphologically symmetric. However, as unwanted protein aggregates are segregated to the cell poles and, after divisions, accumulate at older poles, generate asymmetries in sister cells' vitality. Novel single-molecule detection techniques allow observing aging-related processes in vivo, over multiple generations, informing on the underlying mechanisms. Results: CellAging is a tool to automatically extract information on polar segregation and partitioning in division of aggregates in E.coli, and on cellular vitality. From time-lapse, parallel brightfield and fluorescence microscopy images, it performs cell segmentation, alignment of brightfield and fluorescence images, lineage construction and pole age determination, and it computes aging-related features. We exemplify its use by analyzing spatial distributions of fluorescent protein aggregates from images of cells across generations. Availability: CellAging, instructions and an example are available at
SummaryIn Escherichia coli, under optimal conditions, protein aggregates associated with cellular aging are excluded from midcell by the nucleoid. We study the functionality of this process under sub-optimal temperatures from population and time lapse images of individual cells and aggregates and nucleoids within. We show that, as temperature decreases, aggregates become homogeneously distributed and uncorrelated with nucleoid size and location. We present evidence that this is due to increased cytoplasm viscosity, which weakens the anisotropy in aggregate displacements at the nucleoid borders that is responsible for their preference for polar localisation. Next, we show that in plasmolysed cells, which have increased cytoplasm viscosity, aggregates are also not preferentially located at the poles. Finally, we show that the inability of cells with increased viscosity to exclude aggregates from midcell results in enhanced aggregate concentration in between the nucleoids in cells close to dividing. This weakens the asymmetries in aggregate numbers between sister cells of subsequent generations required for rejuvenating cell lineages. We conclude that the process of exclusion of protein aggregates from midcell is not immune to stress conditions affecting the cytoplasm viscosity. The findings contribute to our understanding of E. coli's internal organisation and functioning, and its fragility to stressful conditions.
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.
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